Yoann Baveye
email: yoann.baveye@univ-nantes.fr

Romain Cohendet
email: romain.cohendet@univ-nantes.fr

Matthieu Perreira
email: matthieu.perreiradasilva@univ-nantes.fr

Da Silva

Patrick Le Callet
email: patrick.lecallet@univ-nantes.fr

Deep Learning for Image Memorability Prediction: the Emotional Bias

Keywords: CCS Concepts, Computing methodologies → Computer vision, Scene understanding, •Human-centered computing → Human computer interaction (HCI), Image memorability, Affect, Deep learning

Image memorability prediction is a recent topic in computer science. First attempts have shown that it is possible to computationally infer from the intrinsic properties of an image the extent to which it is memorable. In this paper, we introduce a fine-tuned deep learning-based computational model for image memorability prediction. The performance of this model significantly outperforms previous work and obtains a 32.78% relative increase compared to the bestperforming model from the state of the art on the same dataset. We also investigate how our model generalizes on a new dataset of 150 images, for which memorability and affective scores were collected from 50 participants. The prediction performance is weaker on this new dataset, which highlights the issue of representativity of the datasets. In particular, the model obtains a higher predictive performance for arousing negative pictures than for neutral or arousing positive ones, recalling how important it is for a memorability dataset to consist of images that are appropriately distributed within the emotional space.

INTRODUCTION

The study of image memorability has recently attracted the interest of computer vision researchers [START_REF] Bainbridge | The intrinsic memorability of face photographs[END_REF][START_REF] Isola | What makes an image memorable[END_REF][START_REF] Mancas | Memorability of natural scenes: The role of attention[END_REF]. Previous work has found that individuals share a tendency to remember and forget the same images [START_REF] Isola | What makes a photograph memorable[END_REF], which paves the way for the design of frameworks predicting the image memorability from intrinsic information. The first attempts at such a prediction used a handcrafted set of low-level features Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. extracted from images to predict their memorability scores (i.e., the degree to which the image is remembered or forgotten) [START_REF] Isola | What makes an image memorable[END_REF]. Results show that image memorability prediction is possible using intrinsic features only. However, this approach performs moderately and could be improved by using higher-level -e.g. semantic -information. In particular, the introduction of deep learning for memorability prediction may disrupt this field of study by substantially increasing the prediction performance of memorability prediction systems.

The second issue addressed in this work is the memorability prediction performance for new images labeled with emotional scores. The emotion a stimulus conveys can be considered as a key element to predict how well it will be memorized. The psychological literature provides evidence that emotional images are generally associated with better memory performance than neutral ones [START_REF] Bradley | Remembering pictures: pleasure and arousal in memory[END_REF][START_REF] Cahill | A novel demonstration of enhanced memory associated with emotional arousal[END_REF][START_REF] Kensinger | Effects of normal aging and alzheimer's disease on emotional memory[END_REF]. However, datasets composed of images associated with memorability scores are not designed according to their distribution in the emotional space.

Our contributions mainly focus on these two aspects and can be summarized as follows. In this work, we introduce "MemoNet": a deep learning-based computational model for image memorability prediction significantly outperforming the performance of the models proposed in previous work. The performance of MemoNet is also evaluated on a new dataset of 150 images, for which we collected through a laboratory experiment emotional ratings and memorability scores. Results show that the performance of the model is tied to the emotional scores of images: the deep learning framework obtains a higher predictive performance for pictures inducing arousing and negative emotions than for pictures inducing neutral or positive ones.

The paper is organized as follows. Section 2 provides background material on image memorability prediction work, as well as Convolutional Neural Networks (CNNs). In Section 3, a deep learning-based framework to predict image memorability is introduced and its performance is compared with previous work. The influence of emotions on the performance of image memorability prediction is studied and discussed in Section 4, while the paper ends in Section 5 with conclusions.

BACKGROUND

Isola et al. pioneered the prediction of image memorability by mapping a combination of global image features using a support vector regression (SVR) [START_REF] Isola | What makes a photograph memorable[END_REF][START_REF] Isola | What makes an image memorable[END_REF]. These global features are standard features that have been previously found to be effective at scene and objects recognition tasks. Indeed, Isola et al. shown that object and scene semantics tends to be a primary substrate of memorability [START_REF] Isola | What makes a photograph memorable[END_REF]. Thus, the global features are GIST, spatial pyramid histograms of SIFT, HOG2x2, SSIM, and pixel color histograms.

More recently, Mancas and Le Meur showed that attentionrelated features can advantageously replace low-level features in image memorability prediction by considerably reducing the size of the input features while performing slightly better [START_REF] Mancas | Memorability of natural scenes: The role of attention[END_REF]. Attention-based features perform 2% better by using 17 dimensions instead of the 512 dimensions of the GIST feature, in addition to the other features introduced in [START_REF] Isola | What makes an image memorable[END_REF], namely SIFT, HOG2x2, SSIM, and pixel color histograms.

Previous work thus relies on a predefined set of handcrafted features extracted from images. However, building complex handcrafted features requires strong domain knowledge and is highly problem-dependent. Obtaining a satisfying feature set is thus not a trivial issue. In contrast with previous work using handcrafted features, in this work we focus on CNNs to predict image memorability scores. Beginning with LeNet-5 [START_REF] Lecun | Backpropagation applied to handwritten zip code recognition[END_REF], CNNs followed a classic structure: they are composed of stacked convolutional layers followed by one or more fully-connected layers. So far, best results on the ImageNet classification challenge have been achieved using CNN-based models [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF][START_REF] Szegedy | Going deeper with convolutions[END_REF]. However, large labeled datasets are crucial to train such large CNN frameworks and there is currently no large image datasets with memorability labels. Nevertheless, fine-tuning is an effective paradigm for learning high-capacity CNNs when data is scarce [START_REF] Girshick | Rich feature hierarchies for accurate object detection and semantic segmentation[END_REF]. The fine-tuning strategy consists in pre-training a deep CNN on a large-scale external dataset (e.g., ImageNet) and fine-tuning the pretrained network by continuing the back-propagation on the small-scale target data to fit the specific classification task.

MEMORABILITY PREDICTION

Fine-tuning pre-trained models originally predicting object and scene semantics is particularly suitable for image memorability prediction since both concepts are inherently related to each other [START_REF] Isola | What makes a photograph memorable[END_REF][START_REF] Konkle | Scene memory is more detailed than you think the role of categories in visual long-term memory[END_REF]. Neural networks have also successfully modeled the biologically inspired memory processes [START_REF] Abraham | Memory retention-the synaptic stability versus plasticity dilemma[END_REF][START_REF] Ans | Neural networks with a self-refreshing memory: knowledge transfer in sequential learning tasks without catastrophic forgetting[END_REF][START_REF] Mcclelland | Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory[END_REF]. Consequently, we fine-tune the GoogleNet model introduced by Szegedy et al., that became in 2014 the new state of the art performance on the ImageNet dataset [START_REF] Szegedy | Going deeper with convolutions[END_REF].

Fine-tuned Convolutional Neural Network

The GoogleNet architecture is a concatenation of nine similar "Inception" networks. An Inception network consists in 1×1, 3×3, and 5×5 convolutions stacked upon each other, with max-pooling layers to reduce the resolution. Given the depth of the network, two auxiliary losses are connected to intermediate layers to increase the back-propagated gradient and to prevent the vanishing gradient problem. During training, the auxiliary losses are added to the total loss of the network with a discount weight. Training is stopped after a specific number of training iterations is reached. At test time, the auxiliary losses are no longer needed and are thus removed from the network. In our fine-tuning approach, the two auxiliary and the final softmax activations are replaced by a fully-connected layer composed of a unique neuron. The loss functions associated to the model are the Euclidean loss.

All the layers of the pre-trained model are fine-tuned, but the learning rate associated to the original layers is ten times smaller than the one associated with the new last neuron. Indeed, we want the pre-trained layers to change very slowly, but let learn faster the new layer. The weights of the new layer are initialized using the xavier algorithm that automatically determines the scale of initialization based on the number of input and output neurons [START_REF] Glorot | Understanding the difficulty of training deep feedforward neural networks[END_REF]. The biases are all initialized as constant, with the default filling value 0.

The proposed fine-tuned model for image memorability prediction is denoted MemoNet in the remaining of this paper.

Experimental Results

In order to obtain results that are fully comparable with previous work, we use in this paper the same data and the same train/test protocol used by Isola et al. [START_REF] Isola | What makes a photograph memorable[END_REF] and Mancas and Le Meur [START_REF] Mancas | Memorability of natural scenes: The role of attention[END_REF]. The dataset is composed of 2,222 images with memorability labels collected by Isola et al. using crowdsourcing [START_REF] Isola | What makes an image memorable[END_REF]. The images were randomly selected from the SUN dataset [START_REF] Xiao | Sun database: Large-scale scene recognition from abbey to zoo[END_REF] and represent various scene categories. Object and scene categories are thus available for each image (see [START_REF] Choi | Exploiting hierarchical context on a large database of object categories[END_REF] for details). A memorability score, used as "ground truth" to train and test MemoNet, is defined as the percentage of correct detections of an image when it is repeated once in a stream of images by a set of participants. MemoNet is trained 25 times using the training sets defined by Isola et al. composed of one half of the images, and tested on the other half of the images.

Similarly to Isola et al., global performance is defined as the mean of the Spearman's Rank Correlation Coefficient (ρ) between the ground truths and the predictions obtained for each of the 25 trained models [START_REF] Isola | What makes an image memorable[END_REF]. In addition, global Mean Square Error (MSE) is assessed. The performance of MemoNet for several training iterations numbers (i.e., 1k, 10k, 30k), as well as the performance of previous work, are indicated in Table 1.

Results show that the performance of MemoNet significantly outperforms the performance obtained by both Isola et al. [START_REF] Isola | What makes a photograph memorable[END_REF] and Mancas and Le Meur [START_REF] Mancas | Memorability of natural scenes: The role of attention[END_REF]. More particularly, MemoNet 30k obtains a 32.78% relative increase in term of Spearman's Rank Correlation Coefficient compared to the approach of Mancas and Le Meur. Table 2 shows the best performing object and scene categories for categories composed of over 100 pictures. Interestingly, best performing object categories are similar to the best performing ones obtained by Isola et al. [START_REF] Isola | What makes a photograph memorable[END_REF]. No significant linear correlation was found between the size of the object categories and their performance (r = -0.018, t(25) = -0.092, p = 0.46).

THE ROLE OF AFFECT

Affect is a key factor in determining the memorability of images [START_REF] Abrisqueta-Gomez | Recognition memory for emotional pictures in alzheimer Šs patients[END_REF][START_REF] Bradley | Remembering pictures: pleasure and arousal in memory[END_REF]. This section thus investigates if the performance of trained models is the same no matter the emotion elicited by the image given as input.

Memorability for IAPS Images

A subset of 150 images randomly selected from the International Affective Picture System (IAPS) dataset [START_REF] Lang | International affective picture system (iaps): Affective ratings of pictures and instruction manual[END_REF] is used in this work to analyze the performance of MemoNet for emotional images. Affective scores are available for these images in terms of valence, arousal, and dominance [START_REF] Ito | Eliciting affect using the international affective picture system: Trajectories through evaluative space[END_REF][START_REF] Lang | International affective picture system (iaps): Affective ratings of pictures and instruction manual[END_REF]. Valence ranges from negative (e.g., sad, disappointed) to positive (e.g., joyous, elated), whereas arousal can range from inactive (e.g., tired, pensive) to active (e.g., alarmed, angry), and dominance ranges from dominated (e.g., bored, sad) to in control (e.g., excited, delighted).

The experimental protocol presented by Isola et al. [START_REF] Isola | What makes an image memorable[END_REF] is reproduced in a laboratory environment to collect memorability scores for each of the 150 selected images. Participants had to perform a memory task and an emotional rating task.

Memory task

The memory task consists of a memory encoding phase interlaced with a recognition memory test. The task instruction is to press the space bar whenever an image reappears in a sequence of images. A black frame is displayed between each image for 1 second. During the task, 50 targets (i.e., images repeated once selected from the subset of 150 images) and 200 fillers are displayed, each of them being displayed for 2 seconds. The fillers, composed of other images randomly selected from IAPS, provide spacing between the first display of a target image and its repetition. Images are displayed pseudo-randomly: the spacing between a target image and its repetition has to be separated by at least 70 images (i.e., 3.30 min) in order to measure memorability corresponding to a long-term memory performance. Whenever the space bar is pushed, the image is framed by a green rectangle to show the participants that their answer is taken into account. Responses that may occur during the 1 second-long inter-stimuli black frame following the target image are also considered. The task is preceded by a training phase to familiarize the participants with the task.

Emotional rating task

An emotional rating task was set up to collect arousal and valence scores for 100 images displayed for 6 seconds. Before the display of each image, the participant is invited to get prepared for the rating process of the next image. The ratings are collected on the 9-point Self-Assessment Manikin (SAM) scales for arousal and valence [START_REF] Bradley | Measuring emotion: the self-assessment manikin and the semantic differential[END_REF], which is a powerful and easy to use pictorial system regardless of age, educational or cultural background due to its non-verbal design. The images are randomly displayed, for a total task duration of about 30 minutes. Similarly to the memorability task, the emotional rating task starts with instructions, followed by a training phase composed of training images spanning the entire arousal-valence emotional space to familiarize the participants with the task but also with the rating scales.

Procedure

The images were displayed on a 40 inch monitor (TV-LOGIC LVM401) with a display resolution of 1,920 × 1,080. The participants were seated at a distance of 150 centimeters from the screen (three times the screen height). The 1,024 × 768 images were centered on a black background; at a viewing distance of 150 cm, the stimuli subtended 18.85 degrees of vertical visual angle. Fifty participants (18-41 years of age; mean = 22.54; SD = 5.01; 60% of them female) compensated for their participation were recruited in Nantes, France. All participants have either normal or correctedto-normal visual acuity. Correct visual acuity was assured prior to this experiment through near and far vision tests using Parinaud and Monoyer charts respectively. The first experimental phase was then launched, corresponding to the memory task. The next day, participants performed the emotional rating task. For each task, displayed images were selected to ensure that each memorability score is generated from at least 16 annotations and that each affective score is generated from at least 32 annotations.

Results

From Section 4.1, a set of 150 images selected from IAPS with memorability and emotional scores is created. The 150 images are pre-processed in order to be given as input to MemoNet 30k. Indeed, MemoNet 30k is fed with the resized 224 × 224 center crop of each image. Black bands added to several pictures by Lang et al. [START_REF] Lang | International affective picture system (iaps): Affective ratings of pictures and instruction manual[END_REF] to obtain the same ratio for each image are removed before cropping the image. As expected, the global performance of MemoNet 30k for this new dataset is lower than the performance of the model measured on the dataset created by Isola et al. [START_REF] Isola | What makes an image memorable[END_REF] (ρ = 0.251; M SE = 0.033).

A local performance of MemoNet 30k is defined as the difference between the ground truth memorability score and the mean of the predictions from the 25 trained models for an image. Table 3 shows the rank correlation between the local performances of MemoNet 30k and the affective scores 1. The rank correlations exhibit a moderate, but coherent, relationship between the performance of MemoNet 30k and the valence, arousal and dominance scores for the emotional ratings collected in both previous work [START_REF] Ito | Eliciting affect using the international affective picture system: Trajectories through evaluative space[END_REF][START_REF] Lang | International affective picture system (iaps): Affective ratings of pictures and instruction manual[END_REF] and our work. Valence is negatively correlated with the local performances of MemoNet 30k, while arousal is positively correlated. Similarly to valence, dominance exhibits a negative correlation with the local performances of MemoNet 30k. Valence and arousal account for most of the independent variance [START_REF] Greenwald | Affective judgment and psychophysiological response: Dimensional covariation in the evaluation of pictorial stimuli[END_REF][START_REF] Lang | International affective picture system (iaps): Technical manual and affective ratings[END_REF]. Consequently, dominance is not taken into account in the following analysis.

To provide a deeper analysis of the relationship between the accuracy of the predicted memorability scores and the affective properties of the pictures collected in this work, the k-means clustering algorithm is used to separate the 150 images into three clusters in the valence-arousal space (see Figure 1(c)). The three clusters separate the images inducing arousing and negative emotions (cluster 1) from the images inducing neutral emotions (cluster 2) and from those inducing moderately arousing and positive emotions (cluster 3). It is important to note that arousal and valences scores of the 150 images are significantly correlated (r = -0.522, t(148) = -7.445, p < .0001). A one-way ANOVA revealed that the local performances of MemoNet 30k is significantly different for the three clusters (F (2, 147) = 5.82; p < .005). The Tukey's multiple comparison test confirmed that the local performances for the pictures in the first cluster is in average closer to zero -i.e. the optimal performance -(mean = 0.0298) than for images in the second (mean = -0.0536) or third (mean = -0.0847) clusters. In other words, this result shows that MemoNet 30k has the highest predictive performance for arousing negative pictures. For the other groups (i.e., neutral and positive) the model is less reliable to predict the memorability.

The results suggest that affect should be taken into account in datasets of images labeled with memorability scores to ensure they induce a large variety of emotions. Indeed, emotion and memorability being related, the performance of a memorability model depends on the emotions induced by the images used to train the model. The results also suggest that emotional information could be a valuable feature to increase the performance of the model for neutral and positive pictures, especially as it is possible to computationally infer emotional information from pictures [START_REF] Liu | Associating textual features with visual ones to improve affective image classification[END_REF][START_REF] Wang | A survey on emotional semantic image retrieval[END_REF].

CONCLUSIONS

The study reported in this paper focuses on the image memorability prediction using deep learning. The proposed model significantly outperforms previous work and obtains a 32.78% relative increase in performance compared to the bestperforming model from the state of the art. An experimental protocol has also been set up to collect memorability and emotional scores in a laboratory environment. However, the generalization of the performance of the deep learning model to this new dataset is a mitigated success. In particular, an emotional bias appears to influence the performance of the proposed model: the deep learning framework obtains a higher predictive performance for arousing negative pictures than for neutral or positive ones. This underlines the importance for an image dataset used for memorability prediction to consist in images appropriately distributed within the emotional space.

Because memorability is also subjective, the memorability prediction is doomed to inaccuracy if one is only interested in the intrinsic information of the images. Our current work focuses on the integration of context-dependent and observerdependent information for the purpose to personalize the memorability prediction.

MM ' 16 ,

 16 October 15-19, 2016, Amsterdam, Netherlands c 2016 ACM. ISBN 978-1-4503-3603-1/16/10. . . $15.00 DOI: http://dx.doi.org/10.1145/2964284.2967269

Figure 1 :

 1 Figure 1: Local performance of MemoNet 30k with (a) valence and (b) arousal scores collected in our experiment for each of the 150 images selected from IAPS, as well as (c) the result of the k-means clustering (k = 3).

Table 1 :

 1 Global performance for image memorability prediction models in terms of Spearman's Rank Correlation Coefficient (ρ) and Mean Square Error (MSE)

	ρ	MSE

Table 2 :

 2 Influence of the object and scene semantics composed of over 100 pictures on the prediction performance of MemoNet 30k. The size and the mean of the memorability scores (GT) for each category are also indicated.

	Rank Category	Size	GT	ρ
	1	person sitting	165	0.753 0.655
	2	person	554	0.725 0.628
	3	pole	108	0.667 0.613
	4	mountain	272	0.593 0.606
	5	painting	101	0.696 0.593
	6	wall	989	0.718 0.581
	7	window	589	0.662 0.580
	8	table	212	0.703 0.580
	9	sign	147	0.664 0.572
	10	door	361	0.668 0.570
	11	chair	268	0.712 0.564
	12	fence	181	0.656 0.554
	13	sky	1080 0.628 0.550
	14	tree	814	0.630 0.548
	15	plant	417	0.640 0.543
	16	floor	766	0.727 0.537
	17	ground	269	0.637 0.521
	18	ceiling	571	0.713 0.514
	19	water	151	0.631 0.511
	20	road	297	0.647 0.497
	21	sidewalk	163	0.643 0.493
	22	building	699	0.630 0.492
	23	ceiling lamp	289	0.713 0.491
	24	box	121	0.719 0.489
	25	steps	115	0.659 0.477
	26	grass	341	0.630 0.464
	27	car	192	0.649 0.464

Table 3 :

 3 Spearman's Rank Correlation Coefficient (ρ) between the local performances of MemoNet 30k for the subset of IAPS images and their affective labels ( * p < .05; * * p < .01; * * * p < .001) collected either in the experiment detailed in Section 4.1, or in previous work[START_REF] Ito | Eliciting affect using the international affective picture system: Trajectories through evaluative space[END_REF][START_REF] Lang | International affective picture system (iaps): Affective ratings of pictures and instruction manual[END_REF]. These correlations measure the relation between the considered emotional dimension and the fact that the model predicts a memorability score higher or lower than the ground truth. Please note that the local performances using Ito et al.'s data are computed on the subset of images from IAPS selected both in this work and Ito et al.'s work, corresponding to 104 images. The local performances of MemoNet 30k and the emotional scores collected in this work are shown in Figure

	Dimension	Data origin	ρ
		Lang et al. [19]	-0.285 * * *
	Valence	Ito et al. [14]	-0.248 * *
		Ours	-0.284 * * *
		Lang et al. [19]	0.096
	Arousal	Ito et al. [14]	0.222 *
		Ours	0.198 * *
	Dominance	Lang et al. [19]	-0.221 * *


Toinon Vigier

Matthieu Perreira

Da Silva

Patrick Le Callet

IMPACT OF VISUAL ANGLE ON ATTENTION DEPLOYMENT AND ROBUSTNESS OF VISUAL SALIENCY MODELS IN VIDEOS: FROM SD TO UHD

Keywords: Visual attention, video, UHD, visual saliency model, eye tracking

The emergence of UHD video format induces larger screens and involves a wider stimulated visual angle. Therefore, its effect on visual attention can be questioned since it can impact quality assessment, metrics but also the whole chain of video processing and creation. Moreover, changes in visual attention from different viewing conditions challenge visual attention models. In this paper, we present a comparative study of visual attention and viewing behavior on three video datasets in SD, HD and UHD conditions. Then, we propose and assess an improvement for video visual attention models by applying a stimulated visual angle dependent center model.

INTRODUCTION

UHD TV standard defines new video technologies as an increasing resolution from HD (1920×1080) to 4K (3840×2160) or 8K (7680×4320). Thus, the emergence of UHD potentially provides a better immersion of the user thanks to a wider visual angle with appropriate larger screens [START_REF] Bt | Parameter values for ultra-high definition television systems for production and international programme exchange[END_REF]. Indeed, ITU defines the optimal viewing distance as the distance at which scanning lines just cannot be perceived with visual acuity of 1'. It is thus set to 6H for SD, 3H for HD and 1.5H for UHD where H is the height of the screen [START_REF]Parameter values for an expanded hierarchy of LSDI image formats for production and international programme exchange[END_REF]. Figure 1 shows the increase of stimulated visual angle along with a better resolution.

Hence, two questions can be raised: 1. Does stimulated visual angle impact viewing behavior and strategies?

2. How can visual angle be taken into account to improve visual attention prediction?

Recent studies highlight the effect of transition from HD to UHD on visual attention [START_REF] Nemoto | Ultra-eye: UHD and HD images eye tracking dataset[END_REF][START_REF] Nemoto | Impact of Ultra High Definition on Visual Attention[END_REF][START_REF] Li | Ultra high definition video saliency database[END_REF]. By comparing visual attention in UHD and HD static images, Nemoto et al. pointed out that viewing strategy and visual attention are significantly different in these two cases: UHD images can grab the focus of attention more than HD images. Moreover, several models of visual saliency were compared in HD and UHD scenarios, showing a reduction of model performance in UHD [START_REF] Nemoto | Impact of Ultra High Definition on Visual Attention[END_REF].

However, viewing behavior in video differs from static images, preventing the straightforward use of these observations for dynamic content. To our knowledge, the first UHD video saliency database was published in [START_REF] Li | Ultra high definition video saliency database[END_REF]. These data come with a comparison of viewing behavior in UHD and HD scenarios. It was especially shown that viewer attention was more focused on the center of the screen in HD context. Nevertheless, no statistical analysis was provided and the performance of visual saliency models in UHD videos was not tackled.

In this paper, we propose to evaluate the impact of stimulated visual angle on visual attention deployment in videos and on the robustness of visual attention models. First, we assess visual patterns by comparing gaze data of three eye tracking datasets in SD, HD and UHD conditions. Then, we present and assess an improvement of video visual attention models by applying a visual angle dependent center model.

MATERIALS AND METHODS

In this section, we present SD, HD and UHD eye tracking datasets and metrics used in this comparative study.

Description of datasets

The used datasets are IVC SD [START_REF] Boulos | Region-of-Interest intra prediction for H.264/AVC error resilience[END_REF], SAVAM [START_REF] Gitman | Semiautomatic visual-attention modeling and its application to video compression[END_REF] and a new UHD dataset, IVC UHD. All these three datasets fulfill the following criteria: public availability, description of the viewing conditions, free-looking approach, no soundtrack, and most importantly, respect of the ITU viewing distance recommendations [START_REF] Winkler | Overview of Eye tracking Datasets[END_REF]. Because there is no public SD, HD and UHD eye tracking datasets sharing exactly the same content, we chose datasets with similar content, i.e. professional videos often used for subjective video quality assessment.

IVC SD is an eye tracking dataset of SD videos compressed in good quality with HEVC. Some of the 31 sequences were presented in two versions: with and without transmission errors. We only use the transmission error-free version.

SAVAM is an HD visual attention dataset on high quality video sequences. Some observers viewed the sequences twice in reverse order. In this case, we only use the first viewing to avoid memory effects.

SD

H α ≈ 12 • α 6 × H HD H α ≈ 30 • α 3 × H UHD H α ≈ 60 • α 1 . 5 × H Fig. 1.
The increase of stimulated visual angle from SD to UHD.

IVC UHD is a new UHD dataset produced in IRCCyN laboratory on 78 reference UHD video sequences including a large variety of scenes. Because of the wider visual angle in UHD, observers can need to move their head and eye tracking systems may not be accurate enough at the edges of the screen. Therefore, we developed a new eye tracking setup EHT (Eye Head Tracking) that is a combination of the mobile SMI eye tracking glasses and of the head tracker Opti-Track ARENA. The experiment was conducted in a test environment set as a standard subjective quality test condition according to ITU-R BT.500 [START_REF]RECOMMENDATION ITU-R BT.500-11 Methodology for the subjective assessment of the quality of television pictures[END_REF]. This new UHD dataset will be available at http://ivc.univ-nantes.fr/.

The detailed description for each dataset is provided in Table 1.

IVC SD SAVAM IVC UHD

Resolution

Saliency maps

We directly compute saliency maps on gaze positions rather than fixations as in [START_REF] Marat | Improving Visual Saliency by Adding 'Face Feature Map' and 'Center Bias[END_REF]. Then, gaze points are convolved with a bidimensional gaussian function with σ = 1°of visual angle as recommended in [START_REF] Le | Methods for comparing scanpaths and saliency maps: strengths and weaknesses[END_REF]. It corresponds to a full width at half maximum (FWHM) of 2.2°which is approximately the size of the fovea. Saliency maps are computed according to the proposed methodology for each video of each dataset.

Metrics

Dispersion

To evaluate the impact of stimulated visual angle on visual attention, we analyze the dispersion of gaze data through two metrics computed for each video of datasets: the mean and the standard deviation of the distances in degree of visual angle between gaze points and the center of the screen over all sequence video frames. In the following, we denote the mean of distances for one video sequence as d seq and the standard deviation of distances for one video sequence as σ dseq .

Comparison with center models

Center bias is a well know phenomenon in visual attention deployment corresponding to the the tendency to gaze mostly at the center of the visual content. This bias would arise from different causes as motor bias, viewing strategy or video content [START_REF] Marat | Improving Visual Saliency by Adding 'Face Feature Map' and 'Center Bias[END_REF][START_REF] Le Meur | Predicting visual fixations on video based on low-level visual features[END_REF][START_REF] Tseng | Quantifying center bias of observers in free viewing of dynamic natural scenes[END_REF]. To evaluate the distribution of gaze points around the center of the video, we compare the experimental saliency maps with center models thanks to Pearson correlation based measures (Cp) and Kullback-Liebler divergence (KLD) as recommended in [START_REF] Le | Methods for comparing scanpaths and saliency maps: strengths and weaknesses[END_REF]. Here, center models correspond to anisotropic 2D gaussian centered in the map. The ratio of the gaussian preserves the ratio of the map. The width is expressed in visual angle degrees and it represents the FWHM of the gaussian. Figure 2 depicts a 10°center model in SD, HD and UHD viewing conditions. Fig. 2. Center models with width = 10°in SD, HD and UHD.

IMPACT OF STIMULATED VISUAL ANGLE ON GAZE DATA DISTRIBUTION

In this section we compare and discuss results obtained on the different datasets with the metrics presented in the previous section. We refer to horizontal stimulated visual angle in degree as α. Figure 3 shows that d seq linearly increases along with the visual angle. The ratio between d seq and α remains nearly constant around 0.18. A Kruskal-Wallis test validates that d seq /α is not significantly different between datasets (p=0.66). Figure 4 shows a strong correlation between σ dseq and α. However, a Kruskal-Wallis test exhibits a slight but significant difference on σ dseq /α between IVC SD and SAVAM and between IVC SD and IVC UHD (p<0.01).

Results on dispersion clearly indicate that observers scan a wider visual angle when stimulated visual angle increases. Nevertheless, the fact that the ratio between d seq and α remains constant, suggests that, until a stimulated visual angle up to 60°, observers scan the same proportion of the image, reaching the same salient region. The slight increase of dispersion from SD to HD and UHD can be explained by a higher inter-observer variability due to an extended freedom of scanpath or a methodology bias due to the difference of sequence length through the datasets.

Comparison with center models

From the KLD and the Pearson correlation between center models of different width and saliency maps, we compute the optimal width of the center model for each dataset. It corresponds to the mean of the width that minimizes KLD and the width that maximizes Pearson correlation coefficient Cp.

Table 2 shows that optimal width increases along with stimulated visual angle but the ratio between the optimal width and α also remains nearly constant around α/3. These results show a linear rule between optimal center model and stimulated visual angle. It confirms the previous assertion that gaze data distribution in video remains relatively stable between SD, HD and UHD viewing conditions. Moreover, it suggests that central bias is largely due to video content rather than motor bias. The optimal width of the center model, α/3, might reflect the rule of thirds in image and video composition.

IMPROVEMENT OF VISUAL SALIENCY MODELS WITH AN OPTIMAL CENTER MODEL

In this section, we develop a method to make visual saliency models more robust toward viewing conditions, by using the fact that deployment of visual attention is proportional to stimulated visual angle.

Proposed method

Some authors show that modulating visual saliency models with a center model enables to simulate central bias improving model performance [START_REF] Marat | Improving Visual Saliency by Adding 'Face Feature Map' and 'Center Bias[END_REF][START_REF] Le Meur | Predicting visual fixations on video based on low-level visual features[END_REF][START_REF] Judd | Learning to predict where humans look[END_REF]. However, the size of center models is rarely motivated. From the results in the previous section, we propose to use viewing conditions and more precisely stimulated visual angle to compute an optimal center model. The FWHM of the gaussian in the optimal center model is set as w opt = α/3. Then we can deduce σ opt as: To assess the optimality of the proposed center model, we confront the performance of visual saliency models with the original saliency map by computing KLD and Cp as described in Section 2.3.2 for 25 videos of the IVC SD and IVC UHD datasets. More precisely, we compare different center, static and dynamic map fusions from the model proposed in [START_REF] Marat | Modelling spatio-temporal saliency to predict gaze direction for short videos[END_REF]. This computational model is a bottom-up visual attention model. The fusion between static and dynamic maps is based on the maximum of the static map a and the skewness of the dynamic map b [START_REF] Satya M Muddamsetty | A performance evaluation of fusion techniques for spatio-temporal saliency detection in dynamic scenes[END_REF].

σ opt = w opt 2 √ 2
M sd = aM s + bM d + abM s × M d
The modulation of the fusion with center model is conducted in two configurations, whether the center model is applied before or after the static-dynamic fusion. 3 show that the proposed center model is always the best predictor in SD and UHD conditions. Most of the time, it significantly outperforms the other center models. The comparison of the two fusion configurations suggests that it is better to modulate maps with central model before fusion. In this case, this simple adaptation permits to improve model performance of more than 100% in SD and around 50% in UHD. All the models (center, static and fusion) obtained better results in SD than in UHD which is consistent with results of [START_REF] Nemoto | Impact of Ultra High Definition on Visual Attention[END_REF] obtained on static images. In this section, we proved that an optimal center model, directly dependent on stimulated visual angle, permits to significantly improve performance of visual saliency models on professional videos. However, other improvements are required to better fit visual attention models to UHD resolution.

Results

Results in Table

CONCLUSION

In this paper, we assessed the impact of visual angle on visual attention deployment. By comparing results on three eye tracking datasets on SD, HD and UHD videos, we showed that the dispersion of gaze points is directly correlated with stimulated visual angle. Results suggest that visual deployment in the video content remains relatively stable until a stimulated visual angle of about 60°. Moreover, we proved that an optimal center model, with a width equal to one third of stimulated visual angle, is the best predictor of visual saliency on professional videos. These results have been successfully applied to make visual saliency models more robust toward viewing conditions by modulating them with this optimal center model.

Fig. 3 .

 3 Fig. 3. Impact of visual angle on d seq .

Fig. 4 .

 4 Fig. 4. Impact of visual angle on σ dseq .

Table 1 .

 1 Description of eye tracking datasets.

		720×576 192×1080 3840×2160
	Width (mm)	287.5	531.6	1422.2
	Distance (mm)	1380	1000	1200
	Distance/Height	6	3.3	1.5
	Horizontal	11.89	29.77	61.30
	visual angle (°)
	Nb of observers	49	48	37
	Nb of sources	31	32	78
	Sequence length	9	18	12
	(sec)

Table 2 .

 2 Optimal center model.

		IVC SD SAVAM IVC UHD
	Optimal width	3.9	9.0	17.7
	Optimal width / α	0.33	0.30	0.29
	KLD (width = α/3) 0.99	1.74	2.23
	Cp (width = α/3)	0.64	0.49	0.52

Table 3 .

 3 Performance of visual attention models with different center models. C i represents a center model of width i°and X Ci represents the X map modulated with the C i center model. S refers to static map and D to dynamic map. Results marked with * are significantly different from the optimal version of the model (non-parametric test).

	ln 2	, thus σ opt ≈	α 7.06

ACKNOWLEDGMENT

The authors wish to thank Josselin Rousseau for his help in generating the eye tracking UHD dataset used in this paper. This work has been supported by the UHD4U project financed by the CATRENE program.


Romain Cohendet
email: romain.cohendet@univ-nantes.fr

Anne-Laure Gilet
email: anne-laure.gilet@univ-nantes.fr

Matthieu Perreira
email: matthieu.perreiradasilva@univ-nantes.fr

Da Silva

Patrick Le Callet
email: patrick.le-callet@univ-nantes.fr

Using individual data to characterize emotional user experience and its memorability: focus on gender factor

Delivering the same digital image to several users is not necessarily providing them the same experience. In this study, we focused on how different affective experiences impact the memorability of an image. Forty-nine participants took part in an experiment in which they saw a stream of images conveying various emotions. One day later, they had to recognize the images displayed the day before and rate them according to the positivity/negativity of the emotional experience the images induced. In order to better appreciate the underlying idiosyncratic factors that affect the experience under test, prior to the test session we collected not only personal information but also results of psychological tests to characterize individuals according to their dominant personality in terms of masculinity-femininity (Bem Sex Role Inventory) and to measure their emotional state. The results show that the way an emotional experience is rated depends on personality rather than biological sex, suggesting that personality could be a mediator in the well-established differences in how males and females experience emotional material. From the collected data, we derive a model including individual factors relevant to characterize the memorability of the images, in particular through the emotional experience they induced.

I. INTRODUCTION

The overwhelming -and growing -number of digital images shared every day makes highly desirable the possibility to automatically predict how relevant they are for the user to whom they are intended. The memorability of an image has recently been proposed as a relevant indicator to help picking out the most meaningful images from a photo collection or a video [START_REF] Isola | What makes an image memorable?[END_REF]. The image memorability consists in the faculty of an image to be recalled after a period of time [START_REF] Mancas | Memorability of natural scenes: The role of attention[END_REF]. It appears from recent studies that it is possible to predict the degree of image's memorability quite well [START_REF] Isola | What makes an image memorable?[END_REF], [START_REF] Isola | What Makes a Photograph Memorable?[END_REF], [START_REF] Khosla | Memorability of image regions[END_REF]. On the other hand, the emotion an image induces can also be regarded as interesting for automatically characterizing its relevance for a particular observer, within a particular context. Indeed, it has been shown that emotional information can be computationally inferred from the image to some extent (for an overview, see [START_REF] Joshi | Aesthetics and Emotions in Images[END_REF]).

While existing research on the prediction of memorability have not taken into account the emotional experience induced by the images, psychological studies investigating the links between emotion and memory suggest that it would be inter-esting. In particular, it has been shown that the valence (i.e. the hedonic tone, ranging from pleasant to unpleasant) of an emotional experience induced by an image is closely related to its memorization (e.g. [START_REF] Charles | Aging and emotional memory: The forgettable nature of negative images for older adults[END_REF]- [START_REF] Mikels | Divergent trajectories in the aging mind: Changes in working memory for affective versus visual information with age[END_REF]). As emotional information can be computationally extracted from image, it could be used to enhance automatic memorability prediction. This would, however, require to know the links between emotional information an image conveys and its memorability.

However, delivering the same image to several users is not necessarily providing them the same experience; accordingly, the memorability of an image, since it depends on the experience, changes in some extent along with individual and context. The consideration of individual factors may help to increase the accuracy of emotional information extraction and memorability prediction, by integrating idiosyncrasy in the equation. To achieve this goal, studies are necessary to better understand how the user factors impact the emotional experience induced by an image and its memorability, and to model them with the aim of integrating information related to the user in the optimization of the digital experience, so that they operate in a personalized way.

In the psychological literature, several individual factors have been highlighted for their impact on both the emotional experience induced by an image and its memorability, including age, gender, affective state (both stable or trait emotions, and current or state ones), and level of fatigue. Some studies have shown that age is related to the way emotional valence of an experience induced by an image is rated. For example, [START_REF] Grühn | Age-related differences in valence and arousal ratings of pictures from the International Affective Picture System (IAPS): Do ratings become more extreme with age?[END_REF] found that older adults perceived negative pictures as more negative than young adults did [START_REF] Grühn | Age-related differences in valence and arousal ratings of pictures from the International Affective Picture System (IAPS): Do ratings become more extreme with age?[END_REF]. Furthermore, the well documented memory boost for negative images sounds to be less important for older adults than for young adults (maybe because of the greater investment in emotion regulation with age) [START_REF] Charles | Aging and emotional memory: The forgettable nature of negative images for older adults[END_REF], [START_REF] Mikels | Divergent trajectories in the aging mind: Changes in working memory for affective versus visual information with age[END_REF].

The literature also suggests that there are differences in the way emotional images are processed by males and females. In particular, females seem to experience the negativity of an image more intensively than males. For example, in a neuroimaging study [START_REF] Wrase | Gender differences in the processing of standardized emotional visual stimuli in humans: a functional magnetic resonance imaging study[END_REF] found that females showed a stronger brain activation for affectively negative pic-tures in the anterior and medial cingulate gyrus [START_REF] Wrase | Gender differences in the processing of standardized emotional visual stimuli in humans: a functional magnetic resonance imaging study[END_REF]. Similarly, in a neurophysiological study [START_REF] Lithari | Are Females More Responsive to Emotional Stimuli? A Neurophysiological Study Across Arousal and Valence Dimensions[END_REF] found that unpleasant stimuli evoked a greater ERP amplitudes in females relatively to males [START_REF] Lithari | Are Females More Responsive to Emotional Stimuli? A Neurophysiological Study Across Arousal and Valence Dimensions[END_REF].

These results seem to suggest that biological sex determines the fact of being more responsive to negative emotional stimuli. However, this difference in sensitivity could be not directly related to biological sex, but to the individual's personality -which could be more or less often associated with an emotional component depending on whether the person is a male or a female -largely dependent on the gender role expected by society [START_REF] Eccles | Gender Role Stereotypes, Expectancy Effects, and Parents' Socialization of Gender Differences[END_REF]. Several questionnaires have been developed to measure gender roles, the most famous being the Bem Sex Role Inventory (BSRI) [START_REF] Bem | On the utility of alternative procedures for assessing psychological androgyny[END_REF], [START_REF] Bem | Bem sex-role inventory: Professional manual[END_REF]. It assesses how individuals identify themselves psychologically, and in particular measures the masculinity-femininity. By using BSRI, one could test if the dominant personality (masculine or feminine) or the biological sex (male or female) have the greatest impact on the way emotional induced by an image is experienced.

Trait emotions may participate in the emotional state induced by an stimulus. An example is the mood-congruence effect, which refers to the tendency of individuals to retrieve information more easily when it has the same emotional content as their emotional state. This bias is typically found in depression, where a depressed mood increases the tendency to remember negative events (e.g. [START_REF] Watkins | Moodcongruent memory in depression: Emotional priming or elaboration?[END_REF], [START_REF] Drace | Evidence for the role of affect in mood congruent recall of autobiographic memories[END_REF]). In the same way, state emotion may play a role in emotion-congruent memory. The priming by a stimulus that has the same emotional content as the target reduces the processing time of the latter, a phenomenon known as the affective priming effect (e.g. [START_REF] Hermans | The affective priming effect: Automatic activation of evaluative information in memory[END_REF]). In a condition where images are displayed in a stream, the emotion induced by an image could, for instance, interfere with the emotional experience induced by the next image.

Finally, it has long been known that the fatigue level decreases brain performance in general, and affects memory and affective state in particular. By way of an extreme example, sleep deprivation is known to have critical effect both on mood and memory [START_REF] Hart | Effect of sleep deprivation on first-year residents' response times, memory, and mood[END_REF].

A. Purpose of the study

In order to better appreciate the underlying idiosyncratic factors that affect the emotional experience induced by an image and its memorability, prior to the test session we collected personal information about the participants (namely, their age, gender, emotional state, fatigue level, and some personality traits) through questions and self-assessment psychological tests. Then participants performed an encoding memory task, in which they saw a stream of images. The next day, they performed a recognition test, where the images viewed the day before were displayed among new images; they had to recognize the target images, and then rate them according to the valence of the emotional experience they induced. In the analysis of the results, we compare biological sex versus gender role influences on how the emotional experience is rated. Then, we derive a model from the data to link memorability to all factors for which we obtained measures.

II. METHOD

Forty-nine participants (18-41 years of age; x = 23.15; SD = 5.56; 59% of them female) compensated for their participation took part in the experiment. They had never participated in a psychological experiment before. The experiment was conducted in two days, in IRCCyN laboratory facilities (Nantes, France), in an experimental room specifically designed and equipped for human assessment of image quality. Upon their arrival, they passed a visual acuity test (Monoyer' scales) and a color vision test (Ishihara color vision test) to ensure no vision problem interfered with the test. Then they answered questionnaires, and performed a memory encoding task in which they saw a stream of images (see Figure 1(a)). The next day, they performed a recognition task in which they had to recognize the 50 target images (i.e.viewed the day before), while they were displayed among 200 new images (see Figure 1(b)). Then, they rated targets according to the positivity/negativity of the emotional experience they induced (see Figure 1(c)).

A. Stimuli and display equipment

In order to induce in participants a wide range of emotions, we used 625 images randomly selected from the International Affective Picture System (IAPS) [START_REF] Lang | International affective picture system (IAPS): Affective ratings of pictures and instruction manual[END_REF]. This database provides researchers with a set of 1196 color standardized photographs (for some modified) rated along the dimension of arousal and valence. The images were displayed on a 40 inch monitor (TV-LOGIC LVM401) with a display resolution of 1920x1080. The participants were seated at a distance of 150 centimeters from the screen (three times the screen height). The 1024x768 images were centered on a black background; at a viewing distance of 150 cm the stimuli subtended 18.85 degrees of vertical visual angle.

1) Questionnaires: To collect participants' personal data on the dimensions we were interested in, we used different psychological tools. We used the BSRI [START_REF] Bem | Bem sex-role inventory: Professional manual[END_REF] to measure the masculinityfemininity. This self-assessment questionnaire consists of 60 items, for which an individual must say how well they match his personality on a scale ranging from 1 (never or almost never true) to 7 (always or almost always true). The measure is based on descriptors that refer to different traits and behaviors that men and women consider valued for a woman or a man -that is to say, the socially accepted gender characteristics. It includes 20 'feminine' items (e.g. gentle, compassionate), 20 'masculine' items (e.g. self reliant, acts as a leader) and 20 filler trait thought to be gender neutral (e.g. sincere, adaptable). The BSRI outputs a categorization of the personality of the individual as 'masculine', 'feminine', 'androgynous' (i.e. both male and female poles highly developed) or 'undifferentiated' (i.e. both male and female poles underdeveloped).

The International Positive and Negative Affect Schedule Short Form (I-PANAS-SF) [START_REF] Thompson | Development and Validation of an Internationally Reliable Short-Form of the Positive and Negative Affect Schedule (PANAS)[END_REF] was used to measure the longterm affective state (trait emotions) of the participant (during the past year). It is a shortened version of the PANAS [START_REF] Watson | Development and validation of brief measures of positive and negative affect: The PANAS scales[END_REF] which contains 10 items, intended to international use with participants whose native language is not English. Participants completing the I-PANAS-SF are asked to rate the extent to which they experience the regarded item on a 7-point Likert Scale ranging from 1 (very slightly/never) to 7 (very much/often). I-PANAS-SF provides results on two dimensions: positive affect and negative affect. The mood matrix proposed by Eich and Metcalfe [START_REF] Eich | Mood dependent memory for internal versus external events[END_REF] was used to assess the participants' current mood at the beginning of each experimental phase. This matrix consists of (9x9) 81 squares; it measures simultaneously the arousal -which corresponds to the degree of physiological activation, ranging from somnolence to high awareness [START_REF] Revelle | The implications of arousal effects for the study of affect and memory[END_REF] -(vertical axis) and valence (horizontal axis) emotional dimensions. The center of the matrix would represent a neutral mood (neither positive nor negative and neither aroused nor unaroused). Participants were asked to tick the square that best reflected their current feelings (e.g. a participant who experienced a positive and highly arousing feelings should tick a square to the top right of the matrix).

For the purpose of measuring the participants' level of fatigue, we used a single item extracted from the PANAS (namely "Indicate the extent you are feeling tired." 1: 'Very tired' to 5: 'Absolutely rest').

Finally, for the emotional rating task, in which the participants had to rate in terms of positivity/negativity the emotional experience induced by the target images, we used the Self Assessment Manikin (SAM) scale for valence [START_REF] Bradley | Measuring emotion: The self-assessment manikin and the semantic differential[END_REF]. This nonverbal pictorial assessment technique consists of an affective rating scale in which degrees are represented by nine graphic figures depicting values along the emotional dimension of valence (ranging from 1 -very negative -to 9 -very positive -, with the figure at the center corresponding to a neutralneither positive nor negative -emotion).

III. RESULTS

We present results in two parts: first, we test the hypothesis that dominant personality (masculine or feminine) affects more than biological sex the way individuals rate the positivitynegativity of an emotional experience; second, we derive a mathematical model from all data to link individual factors to the probability of an image to be recognized when it is repeated. We report the discussion of the results in the next part.

A. BSRI scoring

Participants' self-descriptions through BSRI items were scored using a median-split technique based on scores derived from our sample, as recommended by Bem in 1977 [START_REF] Bem | On the utility of alternative procedures for assessing psychological androgyny[END_REF] following Spence et al.' criticism about her original procedure, which did not discriminate between individuals who scored high, and those who scored low on Feminine and Masculine scales [START_REF] Spence | Ratings of self and peers on sex role attributes and their relation to self-esteem and conceptions of masculinity and femininity[END_REF]. The BSRI categorizes individuals into four possibilities: masculine, feminine, androgynous and undifferentiated. A sex-typed classification (masculine or feminine) is the result of scoring above the median in one gender and below the median in the other. Androgynous categorization occurs when a subject scores above 50% of the group in both masculine and feminine categories. Undifferentiated is the result of scoring below the median in both masculine and feminine categories (seen as the result of extremely low masculine and feminine traits).

The results of the categorization, according to the biological sex of the participants, are reported in table I. We can note that 48% of the male participants were classified into 'Masculine' category, and 24% into 'Feminine' category, while 57% of female participants were classified into 'Feminine' category and 32% into 'Masculine' category.

B. Biological sex and Dominant personality effects on emotional ratings

We performed a two-way ANOVA for unbalanced design (because the number of observations at each factor-level combination were unequal) for testing the effects of biological sex and the dominant personality as assessed by the BSRI (with only the two categories Masculine and Feminine) on the ratings of positivity/negativity of the experiences (see Fig. 2). We observed no significant effect of biological sex (male, female) on valence ratings (F (1) = 0.22, p = .641). On the contrary, we observed a significant effect of the dominant personality (masculine, feminine) on valence ratings (F (1) = 5.29, p < .001), with individuals classified into 'Feminine' category who on average rated the pictures as more negative.

As the literature suggests that females could rate negative images as more negative than males do, because of their greater receptivity to negative stimuli [START_REF] Wrase | Gender differences in the processing of standardized emotional visual stimuli in humans: a functional magnetic resonance imaging study[END_REF], [START_REF] Lithari | Are Females More Responsive to Emotional Stimuli? A Neurophysiological Study Across Arousal and Valence Dimensions[END_REF], we reproduced the same analysis only on the negative images (i.e. for which the mean valence score, calculated from the ratings of all participants, was under the value 5, which corresponds scale to a neutral emotional state on the SAM scale). We observed a similar pattern, with no significant effect of biological sex on valence ratings of emotional experience induced by negative images (F (1) = 0.67, p = .412), but a significant effect of the dominant personality (masculine or feminine) (F (1) = 15.85, p < .001)

C. Regression analysis

For the analysis of influential individual factors, we adopted a Logistic Regression, using the measured individual factors as regressors and the detection of a repetition of a target image (with two possible discrete outcomes, detected or not) as observations to fit. We used the following predictors: the valence rating of the participant, his or her age, biological sex, dominant personality as provided by the BSRI (with the four modalities, masculine, feminine, androgynous and undifferentiated), durable emotional state as measured by the I-PANAS-SF (on the positive and negative dimensions), emotional state at the moment of the task as measured by the mood matrix (on the arousal and valence dimensions), and level of fatigue.

The model, in case of a single observation, can be written as:

y n = β 0 + n i=1 x nk β k + n (1)
where y is the dependent variable -the hit or miss in the detection of the repetition of the image -, x are our predictor values, β are the coefficients to be estimated, and indicates the error term. Even if we expect our model to be more complex than linear, we adopted this method to have interpretable outputs for our features.

The results of the Logistic Regression fit are shown in Fig 3 . Each computed coefficient expresses the influence of an individual factor on the relative chance that a repetition of an image is detected rather than missed. The method also provided a measure of the statistical significance of each feature in the model via their p -value.

In the resulting model, the more positive emotional experience induced by the image is, the less the repetition of the image is likely to be detected. About long-term emotional state, the more positive it is, the more the repetition of an image is likely to be detected, and the more negative it is, the less the repetition of an image is likely to be detected. Finally, the higher the arousal level reported by the participant about his mood at the moment of the task, the more the repetition of an image is likely to be detected. We found no statistically significant effect of age, biological sex, dominant personality, valence of mood at the moment of the task or level of fatigue on the chance for a repeated image to be recognized.

To test our mathematical model, we used a repeated random sub-sampling cross-validation (with 75% of data -i.e. 3875 observations -used for training, and 25% for classification, for each of the 40 splits). We obtained a classification accuracy of 66%, with confidence interval of 3%.

D. Support vector machine

As underlined in [START_REF] Mohammadi | Automatic Personality Perception: Prediction of Trait Attribution Based on Prosodic Features[END_REF], Logistic Regression might result into low accuracies, so it is interesting to complete it with a Support Vector Machine (SVM), which is able to model more complex relations between factors (with the shortcoming that mathematical functions linking inputs and outputs are not easily explainable). In this study, we used a SVM to obtain a baseline, in order to assess the relative efficiency of our model. We adopted a SVM with linear kernel as classifier, using the same data and cross-validation method as in Logistic Regression. We obtained a classification accuracy of 71%, with confidence interval of 3%. This performance gives an insight of how well our regression model fit the data. By losing a bit of performance using regression model rather than a SVM, we nevertheless gain the possibility to interpret the relations.

IV. DISCUSSION

A. Biological sex and Dominant personality effect on emotional ratings

We found no significant difference between males and females in the way they rate positivity/negativity of the experience induced by an image. On the contrary, we found a significant difference between individuals with a masculine dominant personality and those with a feminine dominant personality (according to the BSRI), in the way individuals in Feminine category rated as more negative in general the emotional experiences induced by images. This suggests that personality has a greater impact than biological sex on the way an individual experiences an emotion.

We observed the same pattern (i.e. no effect of biological sex) for negative images, when literature suggests that females feel in general with more intensity the negative experiences (e.g. [START_REF] Wrase | Gender differences in the processing of standardized emotional visual stimuli in humans: a functional magnetic resonance imaging study[END_REF], [START_REF] Lithari | Are Females More Responsive to Emotional Stimuli? A Neurophysiological Study Across Arousal and Valence Dimensions[END_REF]). This seems to be a lack of power problem, since we showed that males are more often classified into Masculine personality and females into Feminine category, and that the masculinity-femininity has an impact on the way valence of the experience is rated. The way we measured the valence of the experience induced by images -i.e. with self-report instrument -could explain a lack of power (the aforementioned studies used direct measures, namely ERPs and Skin Conductance Responses for Lithari et al., fIRM for Wrase et al.).

More generally, the interpretation of BSRI categorizations must be taken with caution: among others, the items are based on gender stereotypes in the American society of the seventies, whose relevance in French society today could be discussed.

B. Modeling

In our regression model, when valence of the experience increases, the probability to detect the repetition of an image decreases, suggesting that negative images are better memorized than others. It has long been known that positive and negative informations are better recognized than neutral ones, suggesting a nonlinear relation between valence and memorability of the images. However, some studies with young adults found that negative information is remembered better than positive information (e.g. [START_REF] Dewhurst | Emotionality, distinctiveness, and recollective experience[END_REF]- [START_REF] Ochsner | Are affective events richly recollected or simply familiar? The experience and process of recognizing feelings past[END_REF]), which is consistent with our results. This negativity bias in memory could disappear with age, as suggested in [START_REF] Charles | Aging and emotional memory: The forgettable nature of negative images for older adults[END_REF], in which the relative number of negative images compared with positive and neutral images recalled decreased with age. About that, it would be interesting to work with older adults than those who passed our experiment (our oldest participant was 41).

When the participant's negative trait emotions increased, the probability to recognize the repeated image decreased; on the contrary, when the participant's positive trait emotions increased, this probability increased. This suggests that negative long-term mood negatively impacts the memory, while a positive mood is rather beneficial; this result is consistent with the fact that depression is often associated with poor memory in general (reviewed by [START_REF] Burt | Depression and memory impairment: A meta-analysis of the association, its pattern, and specificity[END_REF]).

We found that when the arousal level of the emotional state at the moment of the task increased, the probability for a repeated image to be detected also increased, while we found no significant effect for the valence. These results suggest that when an individual is aroused, he has a better memory performance for the image recognition, while the positivity/negativity of its transitory emotional state do not impact, or impact less, this performance. About it, we can talk about potential confounding variables, in particular attention and information processing, associated both with arousal and memory [START_REF] Christianson | The Handbook of Emotion and Memory: Research and Theory[END_REF], which maybe acted as intermediate variables.

Maybe because their relations with recognition memory are not linear, or because of a too limited number of participants in our study, we found no significant effect of age, biological sex, BSRI personality or fatigue on the probability to detect a repeated image using Logistic Regression. For age, the aforementioned fact that only young adults passed our experiment is a shortcoming; indeed, if memory performance decreases in normal aging [START_REF] Hedden | Insights into the ageing mind: a view from cognitive neuroscience[END_REF], this phenomenon does not occur generally before a certain age. About biological sex and dominant personality, we could expect (from the literature and our ANOVA results, respectively) that these variables would play a role in memory for emotional images, because of the different ways these different categories of individuals could experience emotion, and the link between emotion and memory. This absence of significant result is maybe due to the fact that the effect is tenuous, or too complex to be effectively captured by the analysis method we adopted. About fatigue, it sounds from our results that a moderate fatigue (only three participants have reported a low level of fatigue -i.e. less than three on the fatigue scale) does not critically impact memory, such as a great fatigue might do.

V. CONCLUSION

Memorability can be considered as a relevant information in personalized multimedia optimization. In this paper, we focused on individual factors (in particular emotional) it is relevant to take into account to improve the memorability of an experience induced by a digital image. Real-time individual data acquisition is currently booming, in particular emotional data acquisition (under the impulse of recent research interests, such as Affective computing), promising to provide in the near future new efficient tools to capture individual differences in multimedia experience preferences. To take advantage of these technological advancements, it is important that new studies focus on understanding the impact of user factors on multimedia experience appreciation, and develop methods for integrating user information in multimedia system optimization.

Fig. 1 .

 1 Fig. 1. The different experimental tasks. (a) The memory encoding task the first day. (b) The recognition task around 24 hours later (targets are part of the precedent day images). (c) Emotional rating task for previous target images. For the three tasks, the images were displayed in randomized order.

Fig. 2 .

 2 Fig. 2. Mean rating of the emotional experience in function of the biological sex and the dominant personality (error bars represent the standard errors of the mean).

Fig. 3 .

 3 Fig. 3. Features regression coefficients for the probability of detecting the repetition of an image (Features significance: * p < .05, * * p < .001).

TABLE I .

 I DISTRIBUTION OF PARTICIPANTS ACCORDING TO THEIR BIOLOGICAL SEX IN THE CATEGORIES OUTPUTTED BY BSRI.


Toinon Vigier
email: toinon.vigier@univ-nantes.fr

Lukáš Krasula
email: lukas.krasula@univ-nantes.fr

Aurélien Milliat
email: aurelien.milliat@univ-nantes.fr

Matthieu Perreira
email: matthieu.perreiradasilva@univ-nantes.fr

Da Silva

Patrick Le Callet
email: patrick.lecallet@univ-nantes.fr

Performance and robustness of HDR objective quality metrics in the context of recent compression scenarios

The new High Dynamic Range (HDR) technologies intend to increase the immersiveness and quality of experience of the observers thanks to much larger luminance and contrast characteristics. This increase of the amount of information requires the development of new sophisticated compression algorithms. Moreover, well-known objective quality metrics designed for Low Dynamic Range (LDR) systems may not be directly used for assessing HDR videos. To address this issue, several different ways have been proposed. However, their performance and robustness in the context of the most recent HDR compression software solutions need to be assessed and validated on various types of content. In this paper, four objective metrics are benchmarked from data obtained in a new subjective test conducted on various HDR video sequences.

I. INTRODUCTION

High Dynamic Range (HDR) Imaging attempts to capture and reproduce the exact luminance values of the real scene. That enables us to maintain all the details which would be lost in underexposed or overexposed areas if a classical Low Dynamic range (LDR) imaging system was used. However, together with the obvious advantages brought by the HDR technology such as more realistic scene reproduction, higher quality of experience (QoE) of the observers, more space for post-processing, etc., it also brings several drawbacks. One of these is the amount of information necessary for storing the HDR images and videos. The estimated ratio between the file size of a LDR and an HDR image is about 1:4 [START_REF] Banterle | Advanced High Dynamic Range Imaging: Theory and Practice[END_REF].

The development of sophisticated compression algorithms for HDR videos is therefore essential. Currently, a lot of effort has been dedicated to this topic and different schemes for HDR compression have been proposed in order to address the augmentation of information, the augmentation of contrast, the backward compatibility with existing schemes, etc., As a matter of fact, any new video compression technology possibly introduces new artifacts and distortions which brings the need to evaluate their impact on the video quality. This can be done either subjectively or objectively. For the objective quality assessment mainly full-reference objective metrics are used. These metrics are designed to predict video quality by comparing the original video signal against the encoded video signal. In order to address the new possible artifacts, it is necessary to evaluate the performance and the robustness of these metrics in the recent HDR compression scenarios.

The goal of this study is to benchmark the existing objective metrics for video quality and, more specifically, evaluate their robustness in one of the most recent HDR compression solutions. The rest of this paper is organized as follows: Section II describes some previous work on the performance of objective metrics for HDR video quality assessment; Section III presents the subjective experiment; subjective results are analyzed and metrics are benchmarked in Section IV; conclusion ends the paper in Section V.

II. RELATED WORKS

There are two main approaches in full-reference objective HDR image and video quality assessment. In the first one, the metrics are applied on the physical luminance directly. This is in parallel with processing in human visual system (HVS), thus this approach is mostly called HVS-based. The main representatives come from the work of Daly [START_REF] Daly | The Visible Differences Predictor: An Algorithm for the Assessment of Image Fidelity[END_REF] and his Visual Difference Predictor (VDP). The concept was adapted for HDR images by Mantiuk et al. [START_REF] Mantiuk | Predicting visible differences in high dynamic range images: model and its calibration[END_REF] and later on revised in [START_REF] Mantiuk | HDR-VDP-2: A calibrated visual metric for visibility and quality predictions in all luminance conditions[END_REF] and [START_REF] Narwaria | HDR-VDP-2.2: a calibrated method for objective quality prediction of highdynamic range and standard images[END_REF]. The second possibility is to work with the values close to the perceived luminance rather than the physical one. The advantage is that standard full-reference image/video quality metrics can be used. The first HDR metric focusing directly on video quality, HDR-VQM, proposed by Narwaria et al. [START_REF] Narwaria | HDR-VQM: An objective quality measure for high dynamic range video[END_REF] belongs to this category. The approach is dependent on what function is used to obtain the perceived luminance values. One popular approach is employing the logarithmic function with is in line with Weber's law. More sophisticated function, resulting in perceptually uniform (PU) values, was proposed by Aydin et al. [START_REF] Aydin | Extending Quality Metrics to Full Luminance Range Images[END_REF]. It should be noted that no matter which of the approaches is selected, it is of crucial importance to calibrate the metrics according to the truly displayed values depending on the parameters of the screen.

Several studies have attempted to determine the abilities of the metrics regarding HDR compression algorithms. Hanhart et al. [START_REF] Hanhart | HDR image compression: A new challenge for objective quality metrics[END_REF] compared 13 metrics on still images compressed with backwards-compatible JPEG XT profile A algorithm. The best performance was achieved by HDR-VDP-2 [START_REF] Mantiuk | HDR-VDP-2: A calibrated visual metric for visibility and quality predictions in all luminance conditions[END_REF]. Valenzise et al. [START_REF] Valenzise | Performance evaluation of objective quality metrics for HDR image compression[END_REF] showed in a similar study that LDR metrics can perform as well as complicated HVS-based algorithms when the mapping to perceived luminance is performed. Recently, Hanhart et al. compared the performance of 35 objective metrics on 240 compressed HDR images [START_REF] Hanhart | Benchmarking of objective quality metrics for HDR image quality assessment[END_REF]. They showed that HDR-VQM, HDR-VDP-2 and MS-SSIM computed in the PU space are the most reliable predictors of perceived quality.

Metrics performance on HDR video compression has been studied by Azimi et al. [START_REF] Azimi | Evaluating the Performance of Existing Full -Reference Quality Metrics on High Dynamic Range (HDR) Video Content[END_REF]. Here, the best performing method was also HDR-VDP-2 (if only compression is considered). The same result was obtained also by Řeřábek et al. [START_REF] Řeřábek | Subjective and objective evaluation of HDR video compression[END_REF]. First experiment including HDR-VQM performed by its authors in [START_REF] Narwaria | HDR-VQM: An objective quality measure for high dynamic range video[END_REF] showed its superiority over HDR-VDP-2. In [START_REF] Narwaria | Study of high dynamic range video quality assessment[END_REF], the performance of HDR-VQM, HDR-VDP-2.2 [START_REF] Narwaria | HDR-VDP-2.2: a calibrated method for objective quality prediction of highdynamic range and standard images[END_REF], as well as LDR metrics SSIM [START_REF] Wang | Image Quality Assessment: From Error Visibility to Structural Similarity[END_REF] and VIF [START_REF] Sheikh | Image information and visual quality[END_REF] (after the PU mapping) are very similar. In [START_REF] Hanhart | Towards high dynamic range extensions of HEVC: subjective evaluation of potential coding technologies[END_REF], the authors compared the classification errors of various different metrics. In terms of objective measures, PSNR and HDR-VDP-2 can reliably differentiate results between pair comparisons. However, the metric HDR-VQM shows low performance and cannot distinguish difference of quality.

III. SUBJECTIVE EXPERIMENT

A. Dataset

The dataset used for this subjective test consists of HD resolution HDR video sequences from the MPEG Call for Evidence for HDR and WCG video coding [START_REF]/WG11 MPEG[END_REF] and from EBU Zurich Athletics 2014 1 . The length of video sequences was between 4 and 18 seconds. The spatial and temporal information of the HDR reference streams is presented Figure 2 (SI and TI are calculated as described in [START_REF]Subjective video quality assessment methods for multimedia applications[END_REF]). Sources 1 to 5 correspond to EBU streams and sources 6 to 15 correspond to MPEG ones (Figure 1).

B. Compression scenario

We used the test model HEVC Main 10 Profile with six fixed quantization parameters (QP) to compress videos. The coding options are detailed in Table I. The used QP values were 20, 24, 28, 32, 36 and 40 for each content. All video sequences were non-linearly quantized (with PQ EOTF) before compression. Inverse quantization was then applied to transform uncompressed streams to linear RGB OpenEXR format.

C. Test environment

The video sequences were displayed on the SIM2 Solar47 HDR dispay which has a maximum displayable luminance of 4000 cd/m2. The viewing distance was set to three times the height of the screen, i.e., approximately 178 cm.

The experiments was conducted in a test environment equipped with a controlled lighting system of a 6500 K color temperature which fulfills the standard test condition according to ITU-R BT.500 [START_REF]Methodology for the subjective assessment of the quality of television pictures[END_REF].

D. Test methodology

For rating the stimuli, the absolute category rating with hidden reference (ACR-HR) method was used [START_REF]Subjective video quality assessment methods for multimedia applications[END_REF]. It is based on rating sequences independently on a 5-point category scale from 5 (Excellent) to 1 (Bad). The hidden reference condition means that the present test procedure includes a reference (undistorted) version of each source sequence shown as any other test sequence. This methodology allows the direct computation of mean opinion scores (MOS) as well as differential quality score (DMOS) between each test sequence and its corresponding hidden reference. This method is more suitable here because it evaluates perceived quality more than perceived fidelity.

E. Participants

25 remunerated viewers participated in this subjective experiment (13 female and 12 male), aged between 19 and 43 (mean= 26.56 ± 7.39SD). All are non-expert in subjective experiment, image processing or HDR related fields. All participants have either normal or corrected-to-normal visual acuity which was assured prior to this experiment with a Monoyer chart. Ishihara color plates were used to test color vision. All of the 25 viewers passed the pre-experiment vision check.

IV. RESULTS

A. Analysis of subjective results

A post-experiment screening of these subjects was employed to reject any outliers in accordance with the ITU 500 recommandation [START_REF]Methodology for the subjective assessment of the quality of television pictures[END_REF]. None of them was rejected. In addition, as it can be observed in Figure 3, the test was well-balanced: the subjective scores are quite equally distributed across the five classes. The fact that some reference video sequences were not evaluated as Excellent (5) but as Good (4) can explain the superiority of the class 4.

From the observer results, the Mean Opinion Score (MOS) as well as the associated 95% confidence interval were computed for each video stimulus. Figure 4 shows the subjective results for each content.

B. Benchmarking of objective metrics

In this study, the performance of the following full-reference objective metrics is evaluated:

• PU-PSNR (#1): Peak Signal-to-Noise Ratio computed on the perceived luminance (PU mapping),

• PU-SSIM (#2): Signal-to-Noise Ratio computed on the perceived luminance [START_REF] Wang | Image Quality Assessment: From Error Visibility to Structural Similarity[END_REF],

• PU-VIF (#3): Visual Information Fidelity computed on the perceived luminance [START_REF] Sheikh | Image information and visual quality[END_REF],

• HDR-VDP2.2 (#4): High Dynamic Range Visible Difference Predictor for video [START_REF] Narwaria | HDR-VDP-2.2: a calibrated method for objective quality prediction of highdynamic range and standard images[END_REF], Since the first three metrics are originally criteria of image quality, they were calculated per frame and averaged.

To compare the performance of the metrics, criteria described in ITU-T Rec. P.1401 [START_REF]Methods, metrics and procedures for statistical evaluation, qualification and comparison of objective quality prediction models[END_REF] are used. These include Pearson's Linear Correlation Coefficient (PLCC) to measure linearity, Spearman's Rank Order Correlation Coefficient (SROCC) and Kendall's Rank Order Correlation Coefficient (KROCC) to determine the monotonicity, Root-Mean-Squared Error (RMSE) and epsilon-insensitive RMSE (RMSE*), and Outlier Ratio (OR) to determine the accuracy. Before the calculation of PLCC, RMSE, RMSE* and OR, the objective scores were mapped on the subjective scores using 3rd order polynomial monotonic mapping.

Moreover, to gain more insight into the metrics' behavior, the performance evaluation methodology from [START_REF] Krasula | On the accuracy of objective image and video quality models: New methodology for performance evaluation[END_REF] has also been employed. This method provides information about metrics' abilities to distinguish between significantly different and similar videos (Different/Similar analysis) and to recognize which video is better (Better/Worse analysis). The performance is reported in the form of area under curve (AUC) values. Higher AUC value signifies better performance. For the Better/Worse analysis, also the percentage of correct classification (CC 0 ) is reported. For more information about the analyses and interpretation of their results, refer to [START_REF] Krasula | On the accuracy of objective image and video quality models: New methodology for performance evaluation[END_REF].

The results are depicted in Figures 5 and6. Each performance measure is accompanied with the indication if the results are statistically significantly different. White rectangle means that the method in the column performs significantly better than the method in the row. Black rectangle signifies the opposite case and gray rectangle identifies cases where the performances are not significantly different. To compensate for the multiple comparisons, the procedure proposed by Benjamini and Hochberg [START_REF] Benjamini | Controlling the false discovery rate: A practical and powerful approach to multiple testing[END_REF] has been used.

The results indicate that PU-VIF and HDR-VDP2.2 perform significantly better than PU-PSNR and PU-SSIM. The performance of the two best performing metrics are not statistically significantly different with respect to all the performance measures with the exception of C 0 for Better/Worse Analysis, where PU-VIF significantly outperforms HDR-VDP2.2. According to the SROCC, both AUCs, and C 0 , PU-SSIM significantly outperforms PU-PSNR. Its results are also not statistically different from HDR-VDP2.2 in terms of OR. The good performance of HDR-VDP2.2 and PU-VIF is in parallel with the related studies. However, the first one mostly works slightly better than the latter. Our study therefore provides a very interesting outcome, since the results suggest the opposite. Considering the higher computational requirements of HDR-VDP2.2, PU-VIF seems to represent more than adequate alternative.

1) Metrics performance within content: We also decided to check the metrics' abilities within the individual contents. Given that there were only six compressed versions per source content, we only report the results of the AUC measures which allow us to meaningfully congregate the individual results.

The pairs for analyses were therefore taken from within the content only (i.e. no cross-content pairs were considered). The results are depicted in Figure 7. Because of much smaller number of pairs, we were not able to find any statistically significant differences in performance. Nevertheless, we can observe some trends. HDR-VDP2.2 seems to be very reliable in terms of distin-guishing between different and similar videos when no crosscontent pairs are considered. We can also see than all of the metrics are perfectly capable of recognizing more and less distorted versions of the same content (as testified by 100% correct classification in Better/Worse analysis).

2) Metrics performance according to the quality range: Finally, we provide the results per quality range. We split the dataset according to the MOS values into four intervals: [1;2], [2;3], [3;4], [4;5]. Since many of the MOS values in these intervals are expected not to be statistically significantly different, correlation based measures not considering statistical significance would be misleading. We therefore report only the AUC measures again. The results can be found in Figure 8.

With respect to the Different/Similar analyses, no statistically significant differences can be seen. The performances are generally very low (AUC around 0.5 corresponding to the random guessing). In some cases they also drop under 0.5 indicating systematical errors in judgment which videos are qualitatively different and which are similar. For the higher qualities, only HDR-VDP2.2 reaches values higher than 0.5. However, for the lower qualities, its reliability drops. This could have been expected, since the metric is mainly focused on the distortions around the visibility threshold.

In Better/Worse Analysis, the performance of the PU-VIF is significantly better than the rest of the metrics in the lowest quality range. In the higher ranges, results are not significant but generally speaking, PU-VIF and HDR-VDP2.2 work better than the other two criteria. Their performance also never drops under 0.5 which corresponds to the systematical evaluation of the better video as the worse.

V. CONCLUSION

In this paper, we have presented a benchmarking of four objective metrics on a new HDR video dataset. This dataset Fig. 5. Performance of the metrics according to the ITU-T Rec. P.1401 [START_REF]Methods, metrics and procedures for statistical evaluation, qualification and comparison of objective quality prediction models[END_REF] with statistical significance indication. White rectangle means that the metric in the column outperforms the metric in the row, blqck rectangle signifies the opposite case and gray rectangle identifies cases where the performances are not significantly different. Fig. 6. Performance of the metrics according to Krasula et al. [START_REF] Krasula | On the accuracy of objective image and video quality models: New methodology for performance evaluation[END_REF] with statistical significance indication. White rectangle means that the metric in the column outperforms the metric in the row, blqck rectangle signifies the opposite case and gray rectangle identifies cases where the performances are not significantly different. Fig. 7. Performance of the metrics within content according to Krasula et al. [START_REF] Krasula | On the accuracy of objective image and video quality models: New methodology for performance evaluation[END_REF] with statistical significance indication. White rectangle means that the metric in the column outperforms the metric in the row, blqck rectangle signifies the opposite case and gray rectangle identifies cases where the performances are not significantly different.

is composed of 105 HDR video sequences subjectively assessed by 25 observers with ACR methodology. Results show that HDR-VDP2.2 and PU-VIF, i.e. VIF calculated on the perceived luminance obtained with PU mapping, perform the best. Considering the higher complexity of HDR-VDP2.2, PU-VIF seems to be a good alternative to predict perceptual quality of HDR videos. In addition, further analyses in term of metric performance within content show that all the metrics are perfectly enabled to detect the more distorted version between any two of them of the same content. The performance analysis on the different ranges of quality was performed as well, providing more insight into metrics' behavior.

Fig. 1 .

 1 Fig. 1. Tone-mapped and resized versions of the first frames of the 15 reference HDR sources used in the experiment.

Fig. 2 .

 2 Fig. 2. Spatial and temporal information.

Fig. 3 .

 3 Fig. 3. Subjective scores distribution.

Fig. 4 .

 4 Fig. 4. Mean Opinion Scores for each content.

Fig. 8 .

 8 Fig.8. Performance of the metrics for different quality ranges according to Krasula et al.[START_REF] Krasula | On the accuracy of objective image and video quality models: New methodology for performance evaluation[END_REF] with statistical significance indication. White rectangle means that the metric in the column outperforms the metric in the row, blqck rectangle signifies the opposite case and gray rectangle identifies cases where the performances are not significantly different.