Mikołaj Leszczuk

Lucjan Janowski

Marcus Barkowsky

Freely Available Large-scale Video Quality Assessment Database in Full-HD Resolution with H.264 Coding

Keywords: HDTV, free content database, objective evaluation, video quality, video coding impairments

Video databases often focus on a particular use case with a limited set of sequences. In this paper, a different type of database creation is proposed: an exhaustive number of test conditions will be continuously created and made freely available for objective and subjective evaluation. At the moment, the database comprises more than ten thousand JM/x264-encoded video sequences. An extensive study of the possible encoding parameter space led to a first subset selection of 1296 configurations. At the moment, only ten source sequences have been used, but extension to more than one hundred sequences is planned. Some Full-Reference (FR) and No-Reference (NR) metrics were selected and calculated. The resulting data will be freely available to the research community and possible exploitation areas are suggested.

I. INTRODUCTION

Video databases often focus on a particular use case. Databases exist that focus, for example, on the evaluation of coding degradations [START_REF] Urvoy | Nama3ds1-cospad1: Subjective video quality assessment database on coding conditions introducing freely available high quality 3d stereoscopic sequences[END_REF], or transmission degradations [START_REF] Moorthy | Video quality assessment on mobile devices: Subjective, behavioral and objective studies[END_REF], [START_REF] Moorthy | Subjective analysis of video quality on mobile devices[END_REF]. There are also other video quality assessment databases, like the EPFL-PoliMI [START_REF] Simone | Subjective assessment of H.264/AVC video sequences transmitted over a noisy channel[END_REF], [START_REF] Simone | A h.264/avc video database for the evaluation of quality metrics[END_REF] video quality assessment database, the LIVE Video Quality Database [START_REF] Seshadrinathan | Study of subjective and objective quality assessment of video[END_REF], [START_REF] Seshadrinathan | A subjective study to evaluate video quality assessment algorithms[END_REF], the TUM Datasets [START_REF] Keimel | The tum high definition video datasets[END_REF] etc. All these databases have been carefully designed and evaluated using subjective assessment tasks, and are therefore particularly valuable for training objective video quality prediction algorithms or assessing the performance of such a model. Their drawback is that they are small compared to the application area or measurement scope of such algorithms, which will be used for a large variety of different consumer content and which will need to evaluate coding and transmission conditions well outside the design scope of these small databases. These databases are also usually missing several compression parameters for video sequences that are not fully supported by non-reference implementations of the compression standards, notably bit-stream analysers and commercial or free video decoders.

Therefore, in this paper a different type of database creation is proposed: an exhaustive number of test conditions will be created and made available for objective and subjective evalu-ation. At the moment, the proposed Joint Effort Group (JEG) database contains 12,960 encoded video sequences, although so far only coding impairments have been considered and only ten source sequences have been used. It is obvious that such a large number of data-points can no longer be analysed by subjective assessment alone. Reliable objective measurement methods need to be combined to estimate the quality of the largest part of the database. This partly inverts the usual stepwise approach of algorithm development, training, and verification. It poses new challenges, such as estimating the reliability of scores obtained from several models and the possibility to perform algorithms' scope determination based on their congruence with other models. Subjective assessment cannot be completely replaced, but may be reduced to cases in which models produce particular outlier cases. This also leads to establishing a comparably low-cost knowledge base of challenging content and challenging degradations for objective model developers.

This paper describes in detail the process of preparing the test video sequences. The processing method and the chosen parameters will be presented ranging from the preparation of the source sequence to the calculation of several No-Reference and Full-Reference video quality measurement algorithms.

The paper first discusses the general structure of such a database in Section II, and documents the above-mentioned first existing and freely available version in Section III. Several simple Full-Reference (FR) and No-Reference (NR) video quality measurement algorithms have already been evaluated on the database, as presented in Section IV and Section V respectively. Results of correlation analysis of FR metrics will be rapidly presented in Section VI. Possible application areas of this database are then discussed in Section VII.

II. STRUCTURING A LARGE SCALE MODULAR VIDEO DEGRADATIONS DATABASE

It is well known that structuring the contents of a large scale database is crucial to its usage. The current database uses a modular naming approach for the file names which may be extended later if need be. Following a typical transmission chain, video content ('source,' or 'SRC' for short) needs to be encoded using a video encoder, which may or may not contain preprocessing steps such as down-sampling. During the transmission of the bit-stream to the sender, information may be lost, further degrading the video sequence, and then specific decoding and possibly post-processing is required, leading to another class of separate conditions. Finally, during the decoding, different error concealment strategies may be used, which leads to differences between the decoded video and the transmitted bit-stream. These conditions are then globally tabulated in a community accessible document, using, for example, one letter and six digits for specifying the corresponding content, followed by a predefined nomenclature of the corresponding condition, allowing for bijective references. In the SRC the letter refers to the type of content: "professional", "synthetic", "user created". For coding, it refers to the video coding standard, i.e. "H.264", "H.265", and for transmission it refers to the class of transmission system: "packet based". Complex configurations need to be maintained, in particular for the coding conditions, including guidelines to store information about the encoder and its version as well as its execution environment. Virtual machines have proved to be advantageous for preserving exact conditions. Storing the configuration files, file digests and file revision information is considered mandatory.

III. THE JEG DATABASE

This section describes the JEG database. First, the selection of SRC is briefly introduced (Subsection III-A). The creation of Hypothetical Reference Circuits (HRCs) will be then described in detail (Subsection III-B).

A. Source Reference Circuits (SRC)

The current SRC database of JEG comprises about 200 video sequences from which 10 source video sequences (see Fig. 1) have been selected by experts from the Video Quality Experts Group (VQEG) [START_REF]Video Quality Experts Group (VQEG)[END_REF] to serve as a critical minimum dataset. The selection criterion was to establish a database covering as many different features as possible. In particular, synthetic sequences, professionally shot natural content, and user generated content sequences have been chosen. The amount of motion, scene cuts, brightness, high frequency details, etc. has been analysed and equalized. All selected sequences are in progressive format at Full-HD 1920 × 1080 pixels resolution at 25 frames per second. These sequences have been also evaluated on a small set of video conditions in a subjective experiment [START_REF] Barkowsky | Subjective experiment dataset for joint development of hybrid video quality measurement algorithms[END_REF].

B. Hypothetical Reference Circuits (HRC)

This section describes the creation of HRCs. Phases of preprocessing, including resolution change (Subsection III-B1), compression (Subsection III-B2) and post-processing (Subsection III-B3) are detailed.

1) Pre-Processing: SRC have been preprocessed. The process is aimed at the conversion of both a video format and a spatial/temporal resolution. The source video is distributed in AVI file format using YCbCr colour space with 422 colour sub-sampling. For encoding, the colour space needs to be further sub-sampled in order to obtain the 420 input format. For this step, the ImageMagick tool has been used. The ImageMagick tool uses a Lanczos sub-sampling filter for this operation.

2) HRC Generation: The multidimensional coding parameter space was divided into four categories that cover most of the important parameters in terms of video compression (see Table I). Each group of parameters intend to cover extensively the presented parameters. Nevertheless, the full matrix approach was not used since the some combinations are not so commonly used, and the matrix had to be limited.

It should be noted that additional conditions need to be evaluated when analysing and optimizing transmission over error-prone networks.

3) Post-Processing: The ITU reference implementation for H.264 (JM version 18.3) [START_REF] Sühring | H.264/AVC JM Reference Software[END_REF] features compression parameters for video sequences that are not fully supported by other implementations of the standards, notably bit-stream analysers and commercial or free video decoders. Therefore, the decoding of JM-encoded sequences was performed with the reference (JM) implementation as well and the resulting video sequence was transformed into an uncompressed AVI file in order to simplify further analysis of the video quality. Care was taken to avoid unnecessary conversion steps in order that the quality of the sequences could be analysed by calculating objective video quality measurements. As a second encoder implementation, x264 version 0.107.x was chosen. Its output was stored in AVI container format directly in order to reduce file size for downloading the database. Decoding of these videos is believed to provide reliable results by using the libavcodec version 52.72.2 codec library.

IV. FULL-REFERENCE (FR) METRICS EVALUATION

Several simple Full-Reference (FR) video quality measurement algorithms have already been evaluated on the database, as presented in Subsection IV-A (Peak Signal-to-Noise Ratio -PSNR), Subsection IV-B (Structural Similarity Index Metric -SSIM), Subsection IV-C (Video Quality Metric -VQM), and Subsection IV-D (Visual Information Fidelity -VIF). Finally, in Subsection IV-E the diversity and complementarity in terms of modelling different aspects of the Human Visual System (HVS) by these measurement algorithms will be discussed.

A. Peak Signal-to-Noise Ratio (PSNR)

Undoubtedly, one of the simplest, best-known and most often-used measures of video quality is PSNR (Peak Signalto-Noise Ratio). This measure is the ratio of the maximum signal power to the noise power distorting the signal. It is usually expressed as a logarithmic decibel measure [dB]. It is used to determine the visual similarity of the reference and distorted image. It does not apply any perceptual criteria to the interpretation of the perceived noise. We performed PSNR on the Y Signal using 255 as the maximum peak value.

B. Structural Similarity Index Metric (SSIM)

SSIM (Structural Similarity Index Metric) [START_REF] Wang | Video quality assessment using structural distortion measurement[END_REF], [START_REF] Wang | Image quality assessment: from error visibility to structural similarity[END_REF] is a top-down approach which uses a simple functional model of the HVS. Evaluation of quality of video sequences using SSIM is divided into three layers: the local layer of a block-shaped region, the distortion map obtained at frame level, and the layer spanning the complete video sequence. Firstly, from the original and distorted video sequence 8 × 8 pixel blocks are extracted. At this level, the SSIM index is calculated for each block separately for each component of Y , Cb and Cr. In the second step, the local quality values are combined to form a value for the quality level of the frame. The quality of local regions is calculated in accordance with the level of brightness (dark areas are less susceptible to degradation of quality). In the last stage, quality is calculated for the entire video sequence. The quality level of the frame is weighted using frame motion vectors, because some types of distortion (e.g. blur) do not affect the perceived quality of the scenes in the presence of motion [START_REF] Wang | Video quality assessment using structural distortion measurement[END_REF].

C. Video Quality Metric (VQM)

The VQM (Video Quality Metric) method was developed by the Institute for Telecommunication Science (ITS) [START_REF] Winkler | Digital Video Quality -Vision Models and Metrics[END_REF], [START_REF] Wang | Survey of objective video quality measurements[END_REF]. VQM exists in several different implementations that are optimised for specific areas. It takes the spatio-temporal behaviour of the HVS into consideration. As it is freely available, it is often used in comparison studies, and several studies exist which demonstrate that the results of VQM outperform those of PSNR [START_REF] Wolf | Fast low bandwidth video quality model (vqm) description and reference code[END_REF].

D. Visual Information Fidelity (VIF)

VIF (Visual Information Fidelity) is a measure of image quality, which identifies the mutual information shared by the reference and distorted images, with respect to the information contained in the reference image. The term 'information' shall be understood here in its meaning introduced by Shannon. VIF uses NSS (Natural Scene Statistics) modelling together with the image degradation model and the model of HVS [START_REF] Sheikh | Image information and visual quality[END_REF].

E. Complementarity/Diversity of Metrics

PSNR, as a simple energy difference metric, takes into consideration mostly the frame by frame differences between the video frames, and therefore is very accurate on individually distorted frames. SSIM mostly focuses on the texture components of each frame. The processing of both algorithms is straightforward which may improve robustness of prediction performance but may also lack modelling of higher order perceptual features. In particular, the temporal pooling is very simple. VQM works in spatio-temporal blocks and also calculates higher order perceptual features, aiming at modelling the Human Visual System. It has been shown to provide generally higher correlations than PSNR [START_REF] Wolf | Fast low bandwidth video quality model (vqm) description and reference code[END_REF]. VIF follows an information theoretical approach, and therefore may provide complementary results. Its performance has been shown in simulations and studies [START_REF] Sheikh | Image information and visual quality[END_REF].

V. NO-REFERENCE (NR) METRICS AND PARAMETERS EVALUATION

Two simple No-Reference (NR) video quality measurement algorithms have been evaluated on the database. They are presented in Subsection V-A (Blockiness) and Subsection V-B (Blurring).

A. Blockiness

Block artefacts are often caused by the use of lossy compression. This stems from independent coding of "N ×N " pixel blocks (usually 8×8 pixels) in most of the currently used video coding algorithms including H.261 -H.265, MPEG-4 Part 2 and Part 10 or MPEG-2. These algorithms use a quantization of the cosine transform coefficients for each block separately, which causes noise shaping that leads to coding artefacts in the form of discontinuities for coded block boundary. Sudden color intensity changes are most evident in uniform areas of an image and are caused by the removal of the least significant coefficients of DCT. The results of the computed NR metric are elaborated in [START_REF] Romaniak | Perceptual quality assessment for h.264/avc compression[END_REF].

B. Blurring

Blur is caused by the removal of cosine transform coefficients of high frequency, which leads to low-pass filtering. This effect can be seen as a loss of detail in the image, reducing sharp edges and texture of objects. Moderate blur effects may occur due to loop-filters in current encoding standards or due to the combination of image patches from bidirectionally predicted coding-blocks. While these effects usually lead to perceived smoothness for luminance signals, the same effects on chrominance coding may lead to smearing on the edges of areas with contrasting colour values. The results of the computed NR metric are elaborated in [START_REF] Romaniak | Perceptual quality assessment for h.264/avc compression[END_REF].

VI. CORRELATION ANALYSIS OF FR METRICS

As a first example of the usefulness of this database, the previously-mentioned FR measurement algorithms will be compared. Two performance metrics will be used: The Pearson Linear Correlation Coefficient (PLCC), and the Spearman Rank Order Coefficient (SROCC) as described in the VQEG HDTV Test-Plan [START_REF] Cermak | Test Plan for Evaluation of Video Quality Modelsfor Use with High Definition TV Content[END_REF]. Due to the missing alignment, Root Mean Square Error (RMSE) is not appropriate. The results of the currently available FR measurement algorithms are shown in Table II. Please note that they do not indicate any performance of the measurement algorithms in terms of agreement with perceived video quality. The individual scatterplots in Fig. 2 show that the FR measurement algorithm behave very differently. Both from the performance metrics as well as from the scatter-plots it is evident that PSNR behaves differently from the other algorithms. The highest correlation is measured between VQM and VIF which is surprising, taking into consideration that the measurement algorithms use different features. The scatter-plot in Fig. 2(e) reveals that differences exist at any point of the quality scale. For comparing VIF to SSIM, the scatter-plot indicates that a simple linear relationship as assumed by the PLCC may not be sufficient, which is backed up by the high SROCC. In Fig. 2(d) it may be seen that SSIM and VQM behave similar for high quality videos but disagree at lower qualities. Whether this can be explained by the SSIM scale compression towards higher qualities requires further analysis.

VII. POSSIBLE APPLICATIONS

Possible application areas of this database are discussed in this section. These example areas are grouped into development, training and verification of hybrid NR quality models for multimedia content (Subsection VII-A) and subjective evaluation using crowd-sourcing (Subsection VII-B).

A. Development, Training and Verification of Hybrid NR Quality Models for Multimedia Content

In the context of the JEG-Hybrid project of VQEG [START_REF] The | Video Quality Experts Group (VQEG)[END_REF] and SYNAT (System for Science and Technique) project, this dataset will be used for development and training collaboratively developed Hybrid No-Reference models for assessing quality of video sequences. The ground truth for the NR metric development will be associated FR rating data. By combining different measurement methods and evaluating their individual and combined performance, VQEG aims to continuously improve measurement algorithms in a similar way as this has been established for video coding over the last two decades, leading to successive ITU recommendations. As the algorithm development and the training database are open and welcome additions, the project will grow continuously.

B. Subjective Evaluation using Crowd-Sourcing

Subjective evaluation of such a large dataset in controlled lab conditions may be difficult to achieve, even if the number of sequences to be evaluated may be reduced significantly if objective measurements show a high correlation. A possible solution to this problem may be found in the recent advances on crowd-sourcing [START_REF] Keimel | Video quality evaluation in the cloud[END_REF]. Crowd-sourcing is currently considered as a rapid way of obtaining estimations of video quality. While their judgement performance is not as high as those obtained in standardised lab conditions, they may prove particularly useful in the scenario when objective metrics and subjective data are available. Such research is enabled with the proposed large database.

VIII. CONCLUSIONS

The paper proposed a study aimed at identifying the different codec configurations employed in a large video database and applying several quality metrics to study their objective evaluations. At the moment, ten source sequences have been used. After determining all the variables 12,960 JM/x264encoded video sequences were prepared, which were used for calculating metrics. As a first approach to determining the quality and classification parameters for the video sequences, several FR and NR metrics were selected. The resulting data will be freely available to the research community, as they may enable and facilitate research in several domains as hinted in this paper. Researchers interested in obtaining access to the database may contact the JEG-Hybrid project [START_REF] The | Video Quality Experts Group (VQEG)[END_REF]. Possible example application areas of this database are development, training and verification of hybrid NR quality models for multimedia content and subjective evaluation using crowdsourcing.

Fig. 1 .

 1 Fig. 1. Video content (SRC) (source: [10])

Fig. 2 .

 2 Fig. 2. Scatter-plot of unaligned FR measurement algorithms

TABLE I COMPRESSION

 I PARAMETERS' VALUES. ABBREVIATIONS DIA, ESA, AND UMH STANDS FOR: DIAMOND SEARCH WITH RADIUS 1, EXHAUSTIVE SEARCH, AND UNEVEN MULTI-HEXAGON SEARCH, RESPECTIVELY

		Basic compression	Temporal and spatial	Time prediction	I, P, B frame size
			changes		factors
	Bit-rate		1, 2, 4, 8, 16 Mbit/s
	QP		26, 32, 38, 46
	GOP length	8, 16, 32, 64		32, 64
	Number of B frames	0, 2, 3, 7		2
	B-pyramid	strict, none		none
	Frame rate	25	12, 8	25
	Resolution	1920x1080	960x540, 480x270	1920x1080
	Integer pixel motion estima-		Default	dia, esa, umh	Default
	tion method
	Maximum motion vector		Default	4, 64	Default
	search range
	Number of reference frames		Default	4, 16	Default
	Number of slices per frame I to P frame ratio		1, 2 Default		1 0.8, 1, 1.2, 1.4
	P to B frame ratio		Default		0.5, 0.8, 1, 1.2, 1.4

ACKNOWLEDGMENTS

This research work has been supported in part by The Polish National Centre for Research and Development (NCRD) under Grant No. SP/I/1/77065/10 by the Strategic Scientific Research and Experimental Development programme: "Interdisciplinary System for Interactive Scientific and Scientific-Technical Information". This research work has been supported in part by PL-Grid Infrastructure. This research work has been supported in part by AGH University of Science and Technology under Department of Telecommunications statutory work.


Pierre Lebreton
email: pierre.lebreton@telekom.de

Alexander Raake

Marcus Barkowsky

Patrick Le Callet

Measuring perceived depth in natural images and study of its relation with monocular and binocular depth cues

Keywords: Depth perception, monocular, binocular, depth cues

The perception of depth in images and video sequences is based on different depth cues. Studies have considered depth perception threshold as a function of viewing distance (Cutting & Vishton,1995), the combination of different monocular depth cues and their quantitative relation with binocular depth cues and their different possible type of interactions (Landy,1995). But these studies only consider artificial stimuli and none of them attempts to provide a quantitative contribution of monocular and binocular depth cues compared to each other in the specific context of natural images. This study targets this particular application case. The evaluation of the strength of different depth cues compared to each other using a carefully designed image database to cover as much as possible different combinations of monocular (linear perspective, texture gradient, relative size and defocus blur) and binocular depth cues. The 200 images were evaluated in two distinct subjective experiments to evaluate separately perceived depth and different monocular depth cues. The methodology and the description of the definition of the different scales will be detailed. The image database is also released for the scientific community.

INTRODUCTION

The perception of depth refers on different sources of information: the monocular and binocular depth cues. The evaluation and modeling of perceived depth as a function of these cues have received a lot of attention in the field on psychophysics. Considering the evaluation of depth as a function of depth cues, one can quote the work of Bülthoff and Mallot, [START_REF] Bülthoff | Integration of depth modules: stereo and shading[END_REF] Levell, 2 evaluating the relation between binocular disparities and shape from shading, Ernst et al [START_REF] Ernst | Humans integrate visual and haptic information in a statistically optimal fashion[END_REF] regarding haptic and binocular disparities, Girshick and Banks, [START_REF] Girshick | Probabilistic combination of slant information: weighted averaging and robustness as optimal percepts[END_REF] Johnston et al, [START_REF] Johnston | Integration of depth modules: Stereopsis and texture[END_REF] Hillis et al [START_REF] Hillis | Slant from texture and disparity cues: Optimal cue combination[END_REF] regarding texture gradient and binocular disparities, Landy et al [START_REF] Landy | Psychophysical estimation of the human depth combination rule[END_REF] regarding texture gradient and kinetic depth, Wang et al., [START_REF] Wang | Quantifying how the combination of blur and disparity affects the perceived depth[END_REF] Held et al. [START_REF] Held | Blur and disparity are complementary cues to depth[END_REF] regarding blur and binocular disparities. These studies focus either on slant perception or more generally on specifically designed signals rendered via computers such as boxes, disks, meshes, cylinders, spheres, etc. This is one of the restriction that this paper attempt to overcome. Coming from a different community, this paper will address the case of natural images and describe the limits of traditional subjective methods used in psychophysics to this particular study case.

Regarding the modeling, a key reference is the work of Landy et al [START_REF] Landy | Measurement and modeling of depth cue combination: in defense of weak fusion[END_REF] which recall the two main types of models for depth cues fusion into the overall depth perception: the "weak ones" considering the independence of the different depth cues resulting in the linear combination of each individual depth cues and the "strong ones" which allows any types of interaction between depth cues. A new methods was also proposed: the "modified weak fusion" (MWF) which consists of a linear model allowing only one type of interaction: the cue promotion. The cue promotion is a scaling factor applied to one particular depth cue based on parameters determined from the other depth cues. [START_REF] Young | A perturbation analysis of depth perception from combinations of texture and motion cues[END_REF] Bayesian models are also very famous for the combination of sensory inputs. [START_REF] Girshick | Probabilistic combination of slant information: weighted averaging and robustness as optimal percepts[END_REF] These models can be considered either as weak-or strong-fusion approaches depending on the hypothesis made during the definition of the model. In the current paper only linear models will be considered due to their popularity and their good performance in previous studies.

The paper is organized as follow: Section 2 provides information on traditional subjective methods for evaluating depth perception and the contribution of each individual depth cue to the overall depth. It will also provide information on the limits of such methods for the particular study case of natural images. Section 3 describes the different subjective experiments which have been conducted for answering to the proposed research question: evaluating subjectively depth cues and study how to combine them. This section will be divided into three subsections, one will cover the definition of the scales and the two other subsections will provide information on a different experiment to evaluate respectively the overall depth and the monocular depth cues. Section 4 provides information on the result of subjective experiments. Finally, Section 5 will conclude this paper.

SUBJECTIVE TEST METHODOLOGIES

State of the art methodologies

In order to be able to study the combination of different depth cues into the overall depth perception it is needed to have accurate methods for subjective evaluation. This subsection presents different measurement methods reported in the literature. A first method is proposed by Gogel. [START_REF] Gogel | An indirect method of measuring perceived distance from familiar size[END_REF] The idea behind the method is to evaluate the perceived position in depth in an indirect manner using parallax issues. Indeed, if the observer changes his position laterally the objects will appears to shift laterally. The shift is proportional to their distance in depth to the screen and direction of the shift will also be dependent of their position relatively to the display: the shift will be in the same direction as the observer if the object pops-out of the display and will be in the opposite direction if the object is in the zone behind the display. By measuring the displacement and considering the viewing distance it is possible to have a measurement of the position in depth of the objects.

An alternative is proposed by Bülthoff and Mallot. [START_REF] Bülthoff | Integration of depth modules: stereo and shading[END_REF] The idea of this methodology is to ask to the test participants to align several dots on the presented 3D image which is the result of the combination of different monocular and binocular depth cues. The test participant can only adapt the binocular disparity of these dots defining then equivalencies between binocular depth cues only from the dots and the combination of different depth cues on the image (binocular disparity and shadings 1 ).

Another type of approach consists in matching the signal under study to another reference signal. This can be done for example by adapting depth position of markers using only the binocular depth cues and make them match to a surface under study depending on both monocular and binocular depth cues. Another alternative is described by Johnston [START_REF] Johnston | Systematic distortion of shape from stereopsis[END_REF] which is similar to the quality ruler 14 asking test participants to evaluate the test signal on a well controlled scale of other stimuli. Participant should then select which one of the reference stimulus corresponding best to the stimulus under evaluation. The references stimuli can be as described by Johnston 13 different cylinder having continuous curved surface and having different range of depth and the test stimuli are different patterns of randot stereograms.

Other alternatives consist in comparing two different stimuli by a forced choice between different presentations. The different presentation having different properties. These properties can be the larger expansion in depth as in Landy, 10 or which is the tallest as in Ernst et al, [START_REF] Ernst | Humans integrate visual and haptic information in a statistically optimal fashion[END_REF] or which has the biggest slope as in Girshick et al., [START_REF] Girshick | Probabilistic combination of slant information: weighted averaging and robustness as optimal percepts[END_REF] or which is the orientation of the rotation: clockwise or counter clockwise in Braunstein et al. [START_REF] Braunstein | The use of occlusion to resolve ambiguity in parallel projections[END_REF] In the work of Dosher et al [START_REF] Dosher | Tradeoffs between stereopsis and proximity luminance covariance as determinants of perceived 3D structure[END_REF] the task was a forced choice between two different options to explain their understanding of the scene: is the stimulus perceived as a cube or as a truncated pyramid? What is the first direction of the rotation they saw: left or right?

Another approach is to let test participants describe the normal vector to the surfaces as was studied by Stevens and Brook. [START_REF] Stevens | Integrating stereopsis with monocular interpretations of planar surfaces[END_REF] The concept behind this methodology is to ask test participants to define the normal vectors of the surface under study. Alternatively Van Ee 18 let test participant adjust the orientation of lines to describe the slant of the stimulus (a 3D plane).

Another alternative letting more freedom to the test participant is proposed by Tittle and Braunstein [START_REF] Tittle | Recovery of 3-D shape from binocular disparity and structure from motion[END_REF] who asked the observers to report the depth-to-height ratio of the stimuli under study.

Rogers and Graham, [START_REF] Rogers | Similarities between motion parallax and stereopsis in human depth perception[END_REF] chose to increase gradually the amount of binocular disparity and let test participants report from which level of binocular disparity they can perceive depth in the proposed stimulus: randot dots describing an oscillating surface in depth.

Finally, another approach can be found in the litterature as presented by Bruno and Cutting [START_REF] Bruno | Minimodularity and the perception of layout[END_REF] which consists in asking directly to the test participants to evaluate the perceived depth between objects on the rating scale. In the particular case of the study of Bruno and Cutting 21 a scale from 0 to 100 was used, 0 meaning no distance between objects and 100 being the "maximal exocentric separation".

Limitations in the context of the proposed study case

This study focuses on the evaluation of depth from monocular and binocular depth cues in natural images. The use of natural images makes the task particularly difficult since it becomes difficult to define the amount of each depth cue as usually done in psychophysics studies. It is then needed to evaluate how strong each considered depth cue are in order to enable the study of how they affect the overall perceived depth. This comes with a second difficulty: the fact that we may omit to evaluate depth cues which are in the pictures and used by the test participants to evaluate the perceived depth. To limit this, all the different depth cues described by Cutting and Vishton 22 will be considered. The evaluation of the monocular depth cues is challenging; most of the previously described methodologies were designed to evaluate the overall depth perception and not the individual depth cues. Indeed, these individual depth cues could be obtained thanks to the design of the experiment, and there was no need to evaluate these depth cues individually. The methodologies presented in the last subsection which enable the evaluation of the monocular depth cues are forced choice between a certain number of options and the evaluation on a numerical scale. Unfortunately, to enable having a quantitative evaluation of particular scale through a forced choice approach, a large number of comparisons is required. Indeed either the Bradley-Terry model or the Thurstone-Mosteller model [START_REF] Handley | Comparative analysis of Bradley-Terry and Thurstone-Mosteller model of paired comparisons for image quality assessment[END_REF] requires a full pair comparison matrix and then N ×(N -1) 2 comparisons per scales with N the number of stimuli. Optimized approaches are possible using the square design approach [START_REF] Otto Dykstra | Rank analysis of incomplete block designs: A method of paired comparisons employing unequal repetitions on pairs[END_REF] and a well chosen square matrix. [START_REF] Li | Analysis and improvement of a paired comparison method in the application of 3DTV subjective experiment[END_REF] However, considering that natural images were chosen and then the difficulty to define precisely the quantitative amount of monocular depth cues in the stimuli, a large number of images was selected: 200. Then even using the square design approach, the number of required comparison is still too high to be done in a subjective test. This paper restrict then the study to the evaluation on a scale using the absolute category rating (ACR 26 ).

SUBJECTIVE TESTS

In this study three different subjective experiments have been conducted. One targeting the evaluation of the overall depth in stereoscopic still images, and one targeting the evaluation of monocular depth cues in 2D still images.

Image database

In order to evaluate the contribution of monocular depth cues to the overall depth perception in natural images it is necessary to select images having different intensity for each monocular depth cues.

Definition of the scales

Seven different depth cues were considered in this study. Each of them were explained to subjects by a written description, a schema, and several examples. Each of the scales will be described in this section.

The linear perspective Please evaluate the linear perspective (Figure 1) taking into account if there are clear visible vanishing lines within the image and if these vanishing lines contributes to the perception of the different depth layers in the scenes. This depth cues is stronger as clear linear perspective is visible.

The relative size Please evaluate the relative size (Figure 2) by considering if there are repeating objects in the scene which appears with difference size. You should not use your knowledge about the size of the individual objects for the rating. The rate should depend on the number of occurrence an object appears with different size. This depth cue is stronger when objects are repeated with a lot of time at different size. The texture gradient Please evaluate the texture gradient (Figure 3) based on the fact that there is a texture within the image (more generally you can consider the repetition of patterns) which become finer when the distance to the camera increases. This depth cues is stronger when there is a strong variation of the granularity of the texture or pattern.

The interposition Please evaluate the interposition (Figure 4) based on the number of overlapping objects in the scenes. The overlapping of one object over another provides the ability to order the position in depth of the objects. Please evaluate the interposition considering how the number of overlapping object helps to be aware of the absolute position in depth of the objects using all the interpositions. This depth cues is stronger when there is a lot of objects overlapping at different absolute position in depth. The light and shades Please evaluate the light and shades (Figure 5) based the presence of a light source and the resulting shades which helps to apprehend the shape of the objects. This depth cue is stronger when there is a light source which enables to see the real shape of the object which would have appeared flat otherwise.

The areal perspective Please evaluate the areal perspective (Figure 6) based on the effect of the atmosphere in the image. For example, objects which are far away will have a color close to the color of the sky. This depth cue is as strong as there is a smooth transition of the color of the sky to the elements in the background which usually do not have this particular color of the sky. The defocus blur Please evaluate the defocus blur (Figure 7) based on the variation of the sharpness at different location of the image explicating variation of the distance of the object to the focal point of the camera. This depth cue is as strong as there are strong variations between the sharp and blurred area in the images.

Image selection

To perform the image selection process, around 409 images with a large variety of content were evaluated by two expert observers on the 7 different scales as described in the previous subsection on a five grade category scales depicted in the Figures 1234567and an evaluation of the binocular depth quantity on a five grade scale. The images were taken from different open source image and video database [START_REF] Goldmann | Impact of acquisition distortions on the quality of stereoscopic images[END_REF][28][START_REF] Fliegel | Open source database of images DEIMOS: high dynamic range and stereoscopic content[END_REF][30][START_REF] Cheng | RMIT3DV: Pre-announcement of a creative commons uncompressed hd 3D video database[END_REF][32][START_REF] Urvoy | Characterization of NAMA3DS1: a freely available dataset of 3D stereoscopic video sequences[END_REF][34] and images extracted from newly shot video sequences using a Panasonic AG-3DA1E twin-lens Camera, and new images shot with a Fujifilm FinePix Real 3D. After being evaluated on the different scales, it was decided to select four depth cues which will be studied as independently as possible: the linear perspective, the relative size, the texture gradient and the defocus blur. For each of these four depth cues the images were selected such that the score for the six other monocular depth cues was as small as possible and the values of the monocular and binocular depth cues range uniformly distributed from 1 to 5. This results in a matrix of five by five images as shown in Figure 8. Such matrix is then defined for the four previously mentioned depth cues. This results in selecting 100 different images. Considering that the pre-test phase used for the selection of images is only made with two expert observers, it was decided to add a repetition of each combination of monocular and binocular depth cue to increase the robustness of the image selection process and the likelihood to get the expected combination of monocular and binocular depth cues. This finally results in 200 images.

Evaluation of binocular depth

The evaluation of the binocular depth quantity was performed in a first subjective experiment. The 200 still images were used at the highest quality available and did not have any visible coding artefact. Multiple open image and video database, or new recorded content with a video camera or camera the images were used and have many different image formats; The images coming from the camera having an aspect ratio of 4:3 were downscaled from the resolution 3648x2736 to 1440x1080 and were inserted in a uniform gray frame of 1920x1080.

The images coming from the database 3DIQA [START_REF] Goldmann | Impact of acquisition distortions on the quality of stereoscopic images[END_REF]28 where slightly smaller than 1920x1080 and were then centered in a uniform gray frame of 1920x1080. The other images having natively the resolution of 1920x1080 were kept in their original format. The images were presented on a 3D stereoscopic display: Samsung UE46F6500, 46" smart TV with active glasses and a native resolution of 1920x1080. The viewing distance was set to 3H, and the test lab environment was according to the ITU-R BT.500-12 recommendation. [START_REF]Methodology for the subjective assessment of the quality of television pictures[END_REF] Twenty observers attended the experiment; their vision was checked, and it was assured that they passed the color blindness test (Ishihara test) and the depth perception test (Randot stereo test). The observers were trained using five different images with different values of depth quantity. During the training phase the observers had the opportunity to ask questions.

After the training had finished, the observers were asked to rate the 200 images on the amount of perceived depth. The methodology used was Absolute Category Rating (ACR). Perceived depth was rated on a discrete eleven grade scale from 0 to 10 with the labels "very high", "high", "medium", "low" or "very low" -perceived depth respectively at the position 9, 7, 5, 3, 1 on the scale.

Evaluation of monocular depth

Following the discussion provided in Subsection 2.2 the evaluation of the monocular depth cues was performed using the Absolute Category Rating method on a five grade scales to evaluate the seven scales corresponding to each individual depth cue. The test participant were given the instructions described in Subsection 3.1.1, this includes the text describing the depth cues, the pictograms showing the different amount of depth cues and the examples images. The 200 images had the same HD resolution, as described in subsection 3.2 and were displayed on an 9.6 inches iPad 4 with a native resolution of 2048x1536. The images were presented on the top of the interface and were as large as possible and the different scales were represented as pictograms below the image under evaluation. Figure 9 shows the test interface of the application. Test participants evaluated each individual depth cue by selecting the pictograms. The application switched then automatically to the next scale. Test participants were able to edit a previous rating. Once all the seven scales were evaluated, a button appeared allowing the test participant to switch to the next image. For this test, no time constraint was given. The instructions were printed allowing the test participant to refer to them anytime they wanted. For this test, 8 experts in video or audio quality assessment participated to the test. The devices were given to the test participant and the test was performed in a non controlled environment. On average, the test requires 3 to 4 hours and was completed in several sessions within a week at the convenience of the test participants.

RESULTS

Analysis of vote distribution

First, regarding the scores from the 3D viewing session for the 200 images, a histogram built by rounding the mean score over the observer per images to the closest integer value can be found in Figure 10. The purpose of this histogram was to have a view on the distribution of the scores. 50% of the images were rated with a score higher or equal than 5. A Jarque-Bera test shows that the distribution of the subjective scores is not normal at a 95% confidence. Similarly, the histogram of the monocular depth cues scores is depict in Figure 11. The selection of the images was done such that the distribution of the monocular depth cues linear perspective, relative size, texture gradient and defocus blur were meant to cover the entire range. The minimum score for each depth was not frequently used, and the average scores for each depth cue span from 1.5 to 5. For a particular depth cue, it was expected by design that 160 images would have only small amount of this particular depth cues corresponding to the first bin, 10 images would be voted with slight, medium, advanced and strong depth cue respectively, filling the bins 2,3,4 and 5 with 10 samples each. This would have been one of the conditions of a good separation between the depth cues: one depth cue which change of value in a controlled manner from 1 to 5 while all the other depth cues are kept to a minimum value. This kind of result has been achieved for the defocus blur. The areal perspective depth cue shows also a similar pattern. The texture gradient follows this rule to a lower extent. Regarding the linear perspective and the relative size, the distribution is more uniform, this shows that the image selection did not succeed to decorrelate the increase of other depth cues and the increase of relative size or linear perspective. For example the selection of images increasing the amount of texture gradient may have resulted in images having higher amount of relative size. Figure 4.1 depicts the Spearman correlation between the monocular depth cue scores. The correlation values are low, and indicate that the depth cues scores have only little relation between each others. The correlation values however support the discussion about the unexpected distribution of the linear perspective and relative size scores which have a higher correlation between each other showing that these two and the texture gradient and interposition may have shared some images even though the correlation values are too low to be conclusive.

Relation between monocular depth cues and overall depth scores

One of the objectives of the study is to evaluate if in the context of the use of natural images it is possible to show the effect of monocular depth cues on the overall depth score values. One strong limitation of the study is due to the use of natural images which results in the absence of ground truth for the binocular depth cue. Indeed, the subjective data coming from the test in stereo mode described in Subsection 3.2 provides the results of the depth score rating resulting from the combination of monocular depth cues and binocular depth cues. And the test from the subjective test described in Subsection 3.3 only provides monocular depth cue scores. The binocular depth cues themselves could not be controlled as usually done in psychophysics studies since natural image content was used. It is then only possible to use statistics about depth maps and content characteristics as described in previous studies [START_REF] Lebreton | Evaluating depth perception of 3D stereoscopic videos[END_REF] to retrieve information about the binocular depth cues. To analyse the contribution of the monocular depth cue, for each depth cue, two categories are created: one with a low value of the particular depth cue and one with a high value of a depth cue. Let Ω be the set of all images. DC c,high is the set of images such that the depth cue c is high. And ∀I ∈ Ω, DC c (I) is the value of the depth cue c for the image I. DC c,high = {I ∈ Ω|DC c (I) > 3} (1)

DC c,low = {I ∈ Ω|DC c (I) ≤ 3} (2)
IS1(c) = DC c,low i∈{ LP , RS , T G , I , LS , AP , DB }\c DC i,low (3)
Unfortunatly, an ANOVA cannot be performed for each depth cue c in order to compare the depth scores between the two set of images IS1(c) and IS2(c) because the residual of the linear model using only one factor does not fulfill the normality requirements. As shown in Figure 4.1 the correlation between the scales is low, a PCA applied to the data confirms this result and shows that the explained variance increases linearly with the number of support vectors. It is then difficult to decrease the dimensionality. A linear model using all the different variable with no interaction term is then suggested. Using a Jarque-Bera test, it is possible to confirm that the residual error of such model is normal. Table 1 lists the coefficient of the model. It is then possible to apply an N-Way ANOVA to explain the overall depth scores as a function of the monocular depth cue scores.

Only the interposition (F=20.75,p < 0.01) and the defocus blur which is on the borderline was found to have a significant effect (F=3.92,p=0.049). Followed by the texture gradient (F=2.24,p=0.13) and "light and shade" (F=1.6,p = 0.20) which were not significant on a 95% confidence.

Limitations

As mentioned previously, one limitation of the study is dependency to the binocular depth cues which can be hardly evaluated individually in natural images. During the design of the experiments, statistical analysis of the depth map characteristics were done [START_REF] Lebreton | Evaluating depth perception of 3D stereoscopic videos[END_REF] to have a high variety of content's stereoscopic properties, but it remains a factor which needed to be mentioned. An extension of this issue is also the limitation of the study to the particular instantiation of the problem, even though it was targeted by design to cover as much as possible the different monocular depth cue scales. These are limitations that the authors are aware of but which could not be avoided in the targeted challenge.

CONCLUSION

The objective of this paper was to reproduce studies done in the field of psychophysics but in the particular case of the evaluation of monocular and binocular depth cues in natural images. These are preliminary results and details the methodology and questions which have been addressed to tackle this challenge. The different methodologies for the evaluation of depth were explained, but considering the type of stimuli used in psychophysics studies they were not adapted in the particular context of this paper. An alternative methodology was then used and a definition of different scales for the evaluation of monocular depth cues in images was proposed. Various analysis were performed to check the influence of monocular depth cues on the overall depth scores and to see if the methodology used could perform such task. Statistical differences could be seen for the particular case of the interposition and defocus blur depth cues, but not for the other depth cues. The image database including subjective scores will be made available on request to the first author, these can be used for example to study depth in 3D images, but can also be used for the investigation of other aspects such as the effect of coding on depth perception, the acceptance of 3D, the relation between monocular and binocular depth cues and depth quality issues, visual comfort and any other topics related to 3D quality of experience.

Figure 1 .

 1 Figure 1. Linear perspective Figure 2. Relative size

Figure 3 .

 3 Figure 3. The texture gradient Figure 4. The interposition

Figure 5 .

 5 Figure 5. Light and shades Figure 6. Areal perspective

Figure 7 .Figure 8 .

 78 Figure 7. Defocus blur

Figure 9 Figure 10 .

 910 Figure 9. Subjective test interface

Figure 11 .

 11 Figure 11. Histogram of monocular depth cues scores. The vertical line provides the average value of the depth cue.

Figure 12 .

 12 Figure 12. Spearman correlation between monocular depth cues; LP: Linear perspective, RS: relative size, TG: texture gradient, I: Interposition, LS: light and shade, AP: areal perspective, DB: defocus blur

Table 1 .

 1 model = a × LP + b × RS + c × T G + d × I + e × LS + f × AP + g × DB Linear model between depth cues

	a	b	c	d	e	f	g
	2.00	0.44	0.97	2.86	1.41	1.48	1.21


Yaoyao Fiona Zhao

Nicolas Perry

Hery Andriankaja

A Manufacturing Informatics Framework for Manufacturing Sustainability Assessment

Keywords: Manufacturing process, Environmental impact, Life Cycle Assessment, Information Model 1

Manufacturing firms that wish to improve their environmental performance of their product, process, and systems are faced with a complex task because manufacturing systems are very complex and they come in many forms and life expectancies. To achieve desired product functionalities, different design and material can be selected; thus the corresponding manufacturing processes are also changed accordingly. There is direct need of assessment tools to monitor and estimate environmental impact generated by different types of manufacturing processes. This research proposes a manufacturing informatics framework for the assessment of manufacturing sustainability. An EXPRESS information model is developed to represent sustainability information such as sustainability indicators and their associated weighting and uncertainty factors, material declaration information, and hazardous condition information, etc. This information model is tested with industrial products to validate its completeness and correctness. This information model serves as the first step of establishing close association of sustainability information with product design specification. In the next phase of research, investigation will be conducted to integrate sustainability information model and existing standardized product design model ISO 10303 AP 242.

INTRODUCTION

Sustainability is a multi-disciplinary field that involves the areas of ecology, economic development, and social equity [START_REF] Sreenivasan | Sustainability issues in laser-based additive manufacturing[END_REF]. The manufacturing industry is often cited as the cause of many environmental and social problems, yet it is acknowledged as the main mechanism for change through economic growth [START_REF] Baldwin | Modelling manufacturing evolution: Thoughts on sustainable industrial development[END_REF]. It is clear that the world is moving forward aggressively to achieve sustainable design and manufacturing with life cycle considerations. Industry is confronted with the challenge of designing sustainable products and manufacturing processes. The ones who first develop and employ such technologies will gain a competitive advantage in the market place [START_REF] Hoffman | The Green Conversation[END_REF]. Many sustainable development strategies [START_REF] Baas | Cleaner production and industrial ecosystems, a Dutch experience[END_REF][START_REF] Frosch | The industrial ecology of metals: A reconnaissance[END_REF][START_REF] Grant | Planning and designing industrial landscapes for eco-efficiency[END_REF][START_REF] Lowe | Creating by-product resource exchanges: Strategies for eco-industrial parks[END_REF][START_REF] Weizsäcker | Factor Four: Doubling Wealth[END_REF] have been proposed by government agencies and academia since the late 1990s. Research in sustainable manufacturing field can be categorized into the following four main themes [START_REF] Mayyas | Design for sustainability in automotive industry: A comprehensive review[END_REF][START_REF] Seliger | Advances in Sustainable Manufacturing[END_REF]: a) Life-Cycle Assessment (LCA) [START_REF] Ashby | Life-cycle cost analysis: Aluminum versus steel in passenger cars[END_REF][START_REF] Pennington | Life cycle assessment Part 2: Current impact assessment practice[END_REF][START_REF] Saur | Life cycle engineering case study: Automobile fender designs[END_REF][START_REF] Stodolsky | Lightweight materials in the light-duty passenger vehicle market: Their market penetration potential and impacts center for transportation research[END_REF][START_REF] Ungureanu | Design for Sustainability: Product Lifecycle Analysis in Aluminum Auto Body Applications[END_REF], b) design-for-X principles and design for sustainability [START_REF] Barker | Organizing reuse managing the process of design for remanufacture (DFR)[END_REF][START_REF] Bulucea | Building awareness of sustainable automobile manufacturing within industrial ecology framework[END_REF][START_REF] Coulter | Designing for material separation: Lessons from automotive recycling[END_REF][START_REF] Dantec | Analysis of the cost of recycling compliance for the automobile industry[END_REF][START_REF] Hammond | Issues in the automotive parts remanufacturing industry: Discussion of results from surveys performed among remanufacturers[END_REF][START_REF] Jawahir | Design for sustainability (DFS): New challenges in developing and implementing a curriculum for next generation design and manufacturing engineers[END_REF][START_REF] Mildenberger | Planning for an environment-friendly car[END_REF][START_REF] Van Schaik | The effect of design on recycling rates for cars part 1-Theory[END_REF][START_REF] Warburg | Accompanying the (re)design of products with environmental assessment (DfE) on the example of ADSM[END_REF][START_REF] Yuksel | Design of automobile engines for remanufacture with quality function deployment[END_REF]; c) end-of-life studies [START_REF] Govetto | End-of-life vehicle recycling based on disassembly. Determining the Environmental Impact of Disposal[END_REF][START_REF] Jayal | Sustainable manufacturing: Modeling and optimization challenges at the product, process and system levels[END_REF][START_REF] Jody | Product and process design for successful remanufacturing. End-oflife Vehicle Recycling: The State of the Art of Resource Recovery from Shredder Residue[END_REF][START_REF] Palmer | Mechanical Recycling of Automotive Composites for Use As Reinforcement in Thermoset Composites[END_REF][START_REF] Perry | Improving design for recycling -Application to composites[END_REF][START_REF] Sundin | Product and Process Design for Successful Remanufacturing[END_REF]; and d) energy efficiency monitoring and studies [START_REF] Sun | Opportunity Estimation for Real-Time Energy Control of Sustainable Manufacturing Systems[END_REF][START_REF] Tanaka | Review of policies and measures for energy efficiency in industry sector[END_REF][START_REF] Thiede | State of research and an innovative approach for simulating energy flows of manufacturing systems[END_REF][START_REF] Vijayaraghavan | Automated energy monitoring of machine tools[END_REF]. LCA considers all environmental impacts associated with a product or a service from its inception to the end of its life. It can be broken down in five main stages; namely the material extraction, manufacturing, transportation, use, and disposal. International standards for industrial life cycle assessment, such as ISO 14040-14043, have been published in the past decade. These standards lay out the rules that industry should follow for conducting and reporting life cycle assessment. Considering the diversity and complexity of most manufacturing products, LCA methodologies are hampered by two main challenges: a) the diversity and variations in materials, processing techniques, usage durations, and disposal routes; and b) excessive implementation time. Most LCA research is specifically developed for one particular material or product. In industrial setting, LCA is very data-intensive and requires months to complete. Furthermore, LCA is not connected to business perspectives, and thus it does not measure the value of sustainability practice without additional product cost estimation and optimization factors. Design for Sustainability (DfS) aims at designing or re-designing a product in order to reduce its environmental impact within one particular stage of the product life cycle. It complements design-for-X principles. Design for manufacturing methodologies have been used with a focus on cutting both the production lead time and cost. That, in turn, may lead to a reduction in energy consumption.

Design for disassembly, for remanufacturing, and for recycling fall under the umbrella of "design for end-of-life". Design for durability and for energy efficiency is to minimize material usage such as raw material and fossil fuel. While LCA is intended to determine material and energy flows and to assess the resulting environmental impacts, DfS utilizes information and results from LCA to improve product and process design. One key problem with DfS and LCA is the lack of a close connection and integration with other design, management, and manufacturing tools. Energy efficiency research focuses more specifically on -energy consumption monitoring and estimation. These research fields are segmented and they are specific for certain products, materials or processes. The lack of manufacturing system and sustainability information integration hampers the widespread adaptation of the best sustainability practices in the manufacturing industry.

INFORMATION MODELS OF PRODUCT DESIGN AND MANUFACTURING PROCESSES

In recent years, information technology has become increasingly important in the manufacturing enterprise. Effective information sharing and exchange among computer systems throughout a product's life cycle has been a critical issue [36]. Information modeling is a technique for specifying the data requirements that are needed within the application domain [START_REF] Lee | An overview of information modeling for manufacturing systems integration[END_REF]. An information model is a representation of concepts, relationships, constrains, rules, and operations to specify data semantics for a chosen domain of discourse [START_REF] Chen | The entity relationship model: Towards a unified view of data[END_REF]. The advantage of using an information model is that it can provide a sharable, stable, and organized structure of information requirements for the domain context. There are different practices in developing an information model. The underlying methodologies for the recent modeling practices are based on three approaches: the Entity-Relationship (ER) approach, the functional modeling approach, and the Object-Oriented (O-O) approach. Based on these approaches, there are many information models existing in manufacturing industry such as UML models, XML models, IDEF1X models, etc. Amongst these information models, the STEP and STEP-NC information models are the most advanced and standardized ones. STEP and STEP-NC standards have been developed by ISO committee to provide the basis for product design, machining standardization, and integration of part inspections with machining. The information is modeled in EXPRESS language. The purpose of STEP is "to specify a form for the representation and unambiguous exchange of computer-interpretable product information throughout the life of a product" [START_REF]Industrial automation systems and integration -Product data representation and exchange -Part 1: Industrial Automation System and Integration -Product Data Representation and Exchange Part 1: Overview and Fundamental Principles[END_REF]. STEP permits different implementation methods to be used for storing, accessing, transferring, and archiving product data. STEP Application Protocol (AP) 203 edition 1 [START_REF]Industrial automation systems and integration -Product data representation and exchange -Part 203: Application Protocols: Configuration controlled 3D design[END_REF] and edition 2 [START_REF]Industrial automation systems and integration -Product data representation and exchange -Part 203: Application protocol: controlled 3D design of mechanical parts and assemblies[END_REF] (Configuration Controlled 3D Designs of Mechanical Parts and Assemblies) provides the data structures for the exchange of configuration-controlled 3D designs of mechanical parts and assemblies. AP 203 is but one part of the entire ISO 10303 product data standard. It does not present itself as the data standard for configuration management of a product throughout its entire life cycle. The AP is centered on the design phase of mechanical parts and the high-level information entities are shown in Figure 1.

Figure 1: High-level information entities in STEP AP203

Other APs of STEP information models carry the product design data in AP 203 forward through the product life cycle. STEP-NC represents a common standard specifically aimed at NC programming, making the goal of a standardized CNC controller and NC code generation facility a reality [START_REF]Data model for Computerized Numerical Controllers: Part 1 Overview and fundamental principles[END_REF]. It provides CNC with a detailed and structured data interface that incorporates featurebased programming where a range of information is incorporated such as the features to be machined, tool types used, operations (workingsteps) to perform, and process plan (workplan) to develop [START_REF] Zhao | Research into integrated design and manufacturing based on STEP[END_REF] as shown in Figure 2. The feature information in STEP-NC can be obtained by processing the product design geometry in STEP AP 203 through a feature recognition process. The material information and Product Lifecycle Management (PLM) information in STEP AP 203 associated to the design geometry can also be passed onto the manufacturing features in STEP-NC, which in turn associates to the manufacturing process parameters such as machine tools, tool-path, cutting strategy, etc. In this way, the design geometry, material, PLM information is connected to the manufacturing process level.

By providing the high level information to machining systems, STEP-NC will not only eliminate the costly and inefficient process of data post-processing, it will also establish a unified environment for the exchange of information between product design, machining process planning and inspection. It enables the realization of a closed, STEP-NC based machining process chain with data feedback and a consolidated data structure at each level [START_REF] Xu | Advanced Design and Manufacturing Based on STEP[END_REF][START_REF] Xu | STEP-compliant process planning and manufacturing[END_REF]. Industrial use of STEP/STEP-NC has shown evidence of significant cost saving, higher quality, and reduced time-to-market. It may become a major building block in e-economy, the effort to unite manufacturing businesses among corporate partners, distant suppliers, and across divers computer environments [START_REF] Warburg | Accompanying the (re)design of products with environmental assessment (DfE) on the example of ADSM[END_REF].

ISSUES OF INTEGRATING LCA IN PRODUCT DESIGN STAGE

Due to the complexity of manufacturing products and their manufacturing processes, conducting LCA for one product alone is a very time-consuming process. Furthermore, because of the various software and hardware tools involved in a product design and manufacturing process, the quality of LCA data is also a pressing issue for industry to accept current LCA results. The various data format in design and process planning software also contributes to the difficulties of integrating LCA information into product design stage. This section discusses these issues in detail.

Quality and Quality of Data in LCA Process Modeling

Product systems are complex and the manufacturing firms encounter serious problems when using LCA in the design situations for many reasons. Firstly, the large number of parts and the diversity of materials and processes involved inside a product system are true barriers to effective implementation of LCA in the design phase [START_REF] Koffler | Applying voting rules to panel-based decision making in LCA[END_REF][START_REF] Fitch | Life cycle energy analysis as a method for material selection[END_REF]. Collecting and processing all the data needed for modelling the product life cycle does make compiling the inventory a very time consuming task which also requires a lot of efforts [START_REF] Bretz | SETAC LCA workgroup: Data availability and data quality[END_REF]. In addition, these elements are not fully available earlier in the design phase [START_REF] Schiffleitner | ProdTect -Life cycle design and concurrent engineering in the automotive industry[END_REF]. Secondly, it is often difficult to be exhaustive in terms of all the processes, data and parameters of product life cycle modelling as required by LCA procedure [START_REF]Environmental management --Life cycle assessment --Principles and framework[END_REF]. In fact, conducting LCA approach requires that the following four aspects must be taken into account:

1) The level of "depth and detail" of data required in the application(s), 2) The breadth and completeness of the study: life cycle stages, drawn system boundary, inventory and impact category indicators used to meet the intended application(s), 3) The degree of openness and comprehensiveness in the presentation of the results, and 4) The data sources and quality (i.e., are secondary data sources sufficient for the study or primary data required, or a mix of both sources? How much confidence should the users have in the data manipulated and in the study's conclusions? Also, in LCA, the "functional unit" establishes the process parameters. Unfortunately, in real-life complexity, the utility of a product cannot be reduced to the expression of one or two specifications of use [START_REF] Millet | Does the potential of the use of LCA match the design team needs?[END_REF]. However, this rationalization of real-life complexity is acknowledged by qualifying the LCA system boundary as "rigid", as either not all the existing parameters can be reduced to a few numbers and inserted into the process model. This LCA data management is therefore often conducted with a certain degree of subjectivity [START_REF] Leroy | Development of a methodology to reliable environmental decision from life cycle assessment based on analysis and management of inventory data uncertainty[END_REF] and polluted by a lack of comprehensiveness in terms of data sources [START_REF] Ayres | Life cycle analysis: A critique[END_REF]. Indeed, data sources can also contribute to inaccuracy, as it is well known and acknowledged that data used in generic processes may be based on averages, unrepresentative sampling, or outdated measures. Additionally, it is also well known that depending on the scope definition, the considered system boundary may be different from one LCA practitioner to another regarding a product life cycle modelling. Thus, it is never possible to obtain an exact life cycle inventory [START_REF] Bala | Simplified tools for global warming potential evaluation: When 'good enough' is best[END_REF].

LCA Adaptation in the Product Design Stage

Many LCA software tools are commercially available, but SimaPro from Pré Consultants and GaBi from PE International have been around for many years, and are widely used by life cycle assessment professionals. The user interface for both of these products is challenging in terms of intuitiveness.

Concerning the modelling philosophy, GaBi could be qualified as a "process modelling approach", as it requires users to build a "process-tree" to connect materials, energy and processes to parts. After building the product system in GaBi, users can check if inputs and outputs match up for the assemblies. SimaPro could be qualified as a "product modelling approach", as the users are required to enter in data for the product, before switching to a flow chart view to easily see the relative impacts from materials and processes.

Both tools can all the same display inputs/outputs and impacts categories in what appear to be every conceivable format, but the users are responsible for their choices, according to their preferred working philosophy and also related to the nature of the product system needed to be studied. For example, does the product system contain a large amount of parts but involving a few number of processes diversity? Does the product system, whatever the number of components, contain a large diversity of materials and manufacturing processes? Table 1 shows a summary of the relevant advantages and drawbacks from our usage observation of the two software tools described above. It is noticed that we do not intended to compare these tools about commercial aspects such as their costs or the required hardware materials. Behind the choice of LCA tools and softwares, making the LCA method and these sophistically softwares adapted into the design situations is also a veritable challenge. It is due to the problem of time and several efforts needed. Indeed, modelling the product through understanding the implications of system boundaries and the problem of vocabulary gaps between the design and LCA languages make this appropriation impossible for people lacking experience in LCA [START_REF] Dewulf | Exergetic material input per unit of service (EMIPS) for the assessment of resource productivity of transport commodities[END_REF]. Precisely, modelling the product life cycle requires high levels of environmental expertise and specific vocabularies (e.g. unit flow, elementary flow…) to be able to map the product life cycle's interaction with the environment, in terms of extractions and rejects. These notions are still fuzzy among the actors of the design universe [START_REF] Millet | The firm faced to sustainable development: Change of paradigm and learning process. L'entreprise face au développement durable: Changement de paradigme et processus d'apprentissage[END_REF]. Moreover, interpreting the complete and detailed results of LCA is often not helpful in a business context [START_REF] Rebitzer | Enhancing the application efficiency of life cycle assessment for industrial uses[END_REF], since this kind of information can only be understood by an LCA expert, who is not often the decision-maker in the manufacturing firms. It is therefore extremely unrealistic to ask the design actors to model the product directly in LCA software and to interpret the results by themselves.

PROPOSED MANUFACTURING INFORMATICS FRAMEWORK FOR THE INTEGRATION OF LCA AT DESIGN STAGE

In order to integrate LCA information into the product design stage, necessary information must be properly represented and associated to the product PLM information. In the proposed research, a case study was first conducted to examine what LCA information should be modelled. Composite parts give interesting examples on a simple piece such as a pedal crank developed with recycled carbon fibres for thermoset organic matrix composite as shown in Figure 3. During the design, product models have to integrate materials information such as matrix composition, reinforcement type (glass, carbon, aramid, and natural), their architecture (unidirectional, woven) and the structure composition (orientations of the different layers in the depth of the product). Other information like inserts or coating completes the bill of material.

Figure 3: Natec pedal crank made with recycled Carbon Firers Reinforcements (developed with recycled fibres at I2M) [START_REF] Perry | Improving design for recycling -Application to composites[END_REF] Currently in STEP AP 203, only very limited material information is modelled as shown in the following entities:

Figure 2 :

 2 Figure 2: An example of STEP-NC information model in EXPRESS-G diagram

Table 1 :

 1 Advantages and drawbacks of most commonly used LCA software

		GaBi	SimaPro
		Possibility to use parameters
	Advantages	for the calculation and modelling; Implemented sensitivity analysis tool (Monte-Carlo Analysis), scenario analysis, parameter analysis; Easy to identify the critical life cycle stage and associated processes, in terms of impacts; Possibility to create different types of diagrams; High quality LCI database, professional database, wide range data sets cover many	More intuitive interface; Quickly learning how to work; Real-time analysis of impact assessment results; Easy to identify the most impact contributor in terms of part/component; Support damage categories in impact assessment methods; Possibility to create easily his assessment methods; Implemented Ecoinvent databases.
		industrial branches.
	Drawbacks	Do not include the reputed Ecoinvent databases in the professional version; Results visualisation and exploitation are complicated; Less intuitive interface.	Sophisticated analysis option for the assessment results (sensitivity analysis, impacts contribution on each life cycle stages…).

  To associate composite material information to a product design, the following entities were developed in this research. The new entities are written in bold.

	END_ENTITY;
	TYPE material_item_select = SELECT
	(Anisotropic_material,
	Braided_assembly,
	Coating_layer,
	Isotropic_material,
	Laminate_table,
	Part_view_definition,
	Substance_view_definition,
	Woven_assembly);
	END_TYPE;
	ENTITY Composite_material_identification
	SUBTYPE OF (Material_identification);
	DERIVE
	composite_material_name : STRING :=
	SELF\Material_identification.material_name;
	END_ENTITY;
	TYPE material_item_select = SELECT
	(Anisotropic_material,
	Braided_assembly,
	Coating_layer,
	Isotropic_material,
	Laminate_table,
	Part_view_definition,
	Substance_view_definition,
	Woven_assembly
	Composite_material);
	END_TYPE;

ENTITY Material_identification; material_name : STRING; items : SET[1:?] OF material_item_select;

Manufacturing data must also been included in order to ensure the product / material / process combined design in the case of composite parts. Promoting recycled carbon fibres eco-design [START_REF] Perry | Knowledge integration based on road mapping and conceptual framework approach to ease innovation management[END_REF] imposes to access to material data (from end of life scenario and properties) at the early stage of the design. It means developing a specific data storage for material properties (depending to the recycling process) and properties models prediction (based on the different type of fibres) as illustrated on Figure 4. Currently in STEP AP 203, the limited material information, shown above in unbold entities, is not associated to design geometry. The material information is defined only for annotation display. In order to fully integrate material information with design process, the association between product geometry and its material must be semantic. Today no integrated environment allows taking all these facets into consideration. And the next step is to compare, for an expected set of functions, the n-plet [START_REF] Mayyas | Using Quality Function Deployment and Analytical Hierarchy Process for material selection of Body-In-White[END_REF] in order to optimise both technically, but also environmentally with specific focus such as material resources minimization (mix of less material and improved end of life possibilities), energy optimisation and/or pollution reduction. These multi objective optimisation needs to handle all these data, models, life stages, functional unit definition in a coherent environment and at the early stage of product development. STEP and STEP-NC is the most suitable information structure framework that needs to be enriched by the environmental aspects of the product/process data as shown in Figure 5. Eventually, with the proposed information framework, LCA related information is integrated to the design and process planning aspects of product development. The LCA knowledge and data accumulated throughout a product life cycle can be fed back to the product design stage to improve new product development and to reduce environmental impact. Development for some simple case study. Some interoperability problems must be identified between product development models and tools and, Life Cycle Assessment Software and Database. Sustainable product design and manufacturing is the future of today's manufacturing industry. Sustainability research in manufacturing industry has been conducted in several different approaches from life cycle assessment, to design for sustainability, and to energy efficiency analysis. These research fields are segmented and they are specific for certain products, materials or processes. The lack of manufacturing system and sustainability information integration hampers the widespread adaptation of the best sustainability practices in the manufacturing industry. In order to provide product designer with comprehensive material and environmental related information at the design stage, a complete information structure must be developed to associate sustainability information with product design information and product manufacturing process. This research proposes to develop such an information framework based upon STEP and STEP-NC information models. An initial case study was conducted to identify composite material information that should be defined for product design.