Sylvain Tourancheau
email: sylvain.tourancheau@univ-nantes.fr

Patrick Le Callet

Dominique Barba

Image and video quality assessment using LCD: comparisons with CRT conditions

Keywords: subjective quality assessment, LCD motion blur, perception, measurements, displays

In this paper, the impact of display on quality assessment is addressed. Subjective quality assessment experiments have been performed on both LCD and CRT displays. Two sets of still images and two sets of moving pictures have been assessed using either an ACR or a SAMVIQ protocol. Altogether, eight experiments have been led. Results are presented and discussed, some differences are pointed out. Concerning moving pictures, these differences seem to be mainly due to LCD moving artefacts such as motion blur. LCD motion blur has been measured objectively and with psychophysics experiments. A motion-blur metric based on the temporal characteristics of LCD can be defined. A prediction model have been then designed which predict the differences of perceived quality between CRT and LCD. This motion-blur-based model enables the estimation of perceived quality on LCD with respect to the perceived quality on CRT. Technical solutions to LCD motion blur can thus be evaluated on natural contents by this mean.

Introduction

In the recent years, subjective and objective quality assessment becomes a research topic of interest. Activities of the Video Quality Experts Group (VQEG) are a good example of this interest. Previous works [START_REF] Vqeg | Final report from the video quality experts group on the validation of objective models of video quality assessment[END_REF] and work in progress [START_REF] Vqeg | Multimedia group test plan[END_REF] are mainly related to coding or transmitting purpose at a given resolution, e.g. coding artefacts and transmission errors. Display impact is rarely taken in account since for years only CRT were used and the distortions introduced by this technology were considered as insignificant with regards to other artefacts. However, since few years new display technologies have grown such as liquid crystal displays (LCD), plasma or projectors. These new technology are recent and not mature yet comparing to CRT, particularly for moving pictures. As a result, the perceived quality of images and moving pictures can be highly influenced by the display on which they are seen and knowledge about display should be used to improve quality assessment processes. Considering the whole chain, quality assessment should be able to manage dependency to other technology issues. LCD, for example, have many differences with CRT displays. Some subjective studies highlighted a high preference for CRT displays concerning moving pictures [START_REF] Itu | Report on results of comparative subjective picture quality assessment test between CRT and LCD[END_REF]. Many defects have been counted by viewers, such as colour differences, degradations in dark areas and de-interlacing artefacts for interlaced sequences. But among all these defects, motion blur seems to be the most annoying one, particularly in sequences with significant movements. An evidence for motion blur growing interest is the number of studies dealing with its measurement or its analysis [START_REF] Oka | Moving picture response time (MPRT) measurement system[END_REF][START_REF] Klompenhouwer | Temporal impulse response and bandwidth of displays in relation to motion blur[END_REF][START_REF] Pan | LCD motion blur modeling and analysis[END_REF][START_REF] Feng | LCD motion-blur analysis, perception, and reduction using synchronized backlight flashing[END_REF][START_REF] Li | LCD motion artifact determination using simulation methods[END_REF][START_REF] Watson | The Spatial Standard Observer: A human vision model for display inspection[END_REF][START_REF] Becker | Motion blur measurement and evaluation: From theory to the laboratory[END_REF][START_REF] Feng | Comparison of motion blur measurement in lcd[END_REF]. Moreover, standardisation organisms such as TCO Development [START_REF] Tco | TCO'06 Media Displays[END_REF] and VESA [START_REF] Vesa | Flat Panel Display Measurements (FPDM2)[END_REF] are currently working on requirements concerning this particular distortion. In this paper, some subjective quality assessment tests are described, as well with still pictures as with mov-1 ing ones. Both LCD and CRT displays are used and subjective scores are compared and discussed. In a second part, LCD motion blur is presented. Psycho-physics experiments and physical measurements are presented. Finally, an example of objective metric based on display knowledge is addressed. This prediction model is based on LCD motion blur and permits to estimate the perceived quality on LCD from the perceived quality on CRT.

Subjective quality assessment experiments

In this section, some subjective quality assessment experiments are described. They have been performed on both CRT and LCD displays, and considering still images as well as moving pictures.

Image and video materials

Four sets of materials have been used in the subjective quality assessment experiments. Two sets named SP1 and SP2 contain only still images while two other sets, MP1 and MP2, contain moving pictures:

• SP1: the Toyama database, i.e. 14 colour pictures (768×512), coded with JPEG2000 using 6 compression ratio, and with JPEG using 6 compression ratio (196 pictures).

• SP2: 5 colour pictures (1280×800), distorted using mainly several combinations of downscaling followed by up-scaling operations (20 pictures).

• MP1: 10 HDTV sequences (1920×1080 interlaced, YUV 4:2:2) of 10-second length from the European broadcasters SVT and Euro1080, coded with H.264 using 7 different bit-rates (80 sequences).

• MP2: 4 of the latter ones, rescaled in a SDTV-similar format (960×540 interlaced), and coded with H.264 using 6 different bitrates (24 sequences).

Viewing conditions and observers

All subjective quality assessment experiments have been performed in a standardised room. Room illumination, chromaticity background, display settings and viewing distance have been set according to ITU recommendations BT.500-11 [START_REF] Itu | Methodology for the subjective assessment of the quality of television pictures[END_REF] and BT.710-4 [START_REF] Itu | Subjective assessment methods for image quality in high-definition television[END_REF]. These parameters have been adjusted, when needed, for different contents and displays used in the experiments. For example, viewing distance was set to 3H for HDTV content, 6H for SDTV content and 4H for Toyama database images, where H is the height of the displayed picture. For experiments using moving pictures, a HD player has been used, which enables the realtime playback of uncompressed HDTV content. In all experiments, the same displays has been used: a JVC DT-V 1910CG (CRT) and a Philips T370 HW01 (LCD). Each observer participating in the experiments was first checked for colour blindness with Ishihara test and for visual acuity with Monoyer's plates. People with at least one error in Ishihara's test or less than 9/10 in Monoyer's test were rejected. Observers'age ranged from 20's to 60's with an average around 30. Gender parity was respected as well as possible. The consistency of the individual scores was then evaluated after the tests have been completed by all the valid observers. It was done by applying a suitable rejection technique. This is a process in which all scores from a particular subject are omitted from the analysis of data. The number of retained observers depends on the protocol used (see 2.3).

Protocols

An absolute category rating (ACR) protocol has been used with the Toyama database (SP1 material) to follow the same test conditions as for experiments performed on CRT on a previous paper from Parvez et al. [START_REF] Sazzad | Spatial features based no reference image quality assessment for JPEG2000[END_REF]. With this protocol, each picture is presented singly for subjective assessment and the observer is asked to provide his perception of the quality on a discrete scale that is divided into five numerical values and adjectives (Bad =1, Poor=2, Fair=3, Good=4, and Excellent=5). The test presentation order is randomised differently for each observer. Following the VQEG recommenda-tions [START_REF] Vqeg | Multimedia group test plan[END_REF], at least 24 observers should be retained after the rejection process.

An effect of quantification of the subjective scores can appear with the ACR protocol and its discrete quality scale. In order to obtain more precise and more reliable results, we decided to use the SAMVIQ protocol for the three others sets of materials (SP2, MP1 and MP2). The SAMVIQ method [START_REF] Ebu | SAMVIQ -Subjective assessment methodology for video quality[END_REF] is a multi-stimuli continuous quality scale (MSCQS) protocol developed by France Telecom R&D and standardised by the European Broadcasting Union (EBU) and by the International Telecommunications Union (ITU). It provides a precise and reliable measure of the subjective video quality as the observers have a random access to all sequences and can directly compare the impaired sequences among themselves and against the reference. This allows them to precisely build their assessment opinion. This is particularly interesting in a HDTV context where very little quality differences have to be identified [START_REF] Blin | New quality evaluation method suited to multimedia context: Samviq[END_REF]. Observers set the quality score using a continuous scale with a cursor moving from 0 to 100. Five adjectives were displayed on the scale to help the observers'choice (Bad =10, Poor=30, Fair=50, Good=70, and Ex-cellent=90). At least 15 observers should be retained after the rejection process.

Results on still images (SP1 and SP2)

2.4.1 Toyama JPEG and JPEG2000 databases (SP1)

The Toyama database has been assessed on CRT in the University of Toyama in Japan [START_REF] Sazzad | Spatial features based no reference image quality assessment for JPEG2000[END_REF]. In order to compare both displays, we decided to perform the same subjective quality assessment using a LCD. However, design the same experiment in two different labs it's a real challenge and the so-called "lab effect" can occur. Actually, even if set as similar as possible, the viewing conditions can differ from on testing room to another. Furthermore, using two different pools of observers can also lead to slight differences. Also, some cultural differences can appear between France and Japan, in the way to assess quality. For example, the way that observers consider the adjectives on the quality ladder can be different. These adjectives were in English for the experiments in Japan, which was not the native lan- guage of observers. They were translated in french for the experiments in France but the meaning of words can be slightly altered from one language to another.

Results between the two labs should be compared with regard to both the difference of displays and the lab effect. Figures 1 and2 present the mean opinion scores (MOS) obtained on LCD in our lab as a function of MOS obtained on CRT in the University of Toyama [START_REF] Sazzad | Spatial features based no reference image quality assessment for JPEG2000[END_REF] for the JPEG database and the JPEG2000 database respectively. In both cases, the set of scores obtained on CRT is are highly correlated with the one obtained on LCD, with linear correlation coefficients of 0.956 for the JPEG database and 0.966 for the JPEG2000 database. It's remarkable to have such a important correlation between experiments performed separately. However, when looking more precisely at the scores, a difference of scale can be observed. For both databases, the scores obtained on LCD (in France) are slightly better in the low part of the quality scale, whereas they are slightly lower in the high part of the quality scale.

HDTV format pictures (SP2)

Images of SP2 set contain specific contents such as natural textures, flesh colours, oriented contours, water reflection, characters, etc. Each of them has been distorted by different combinations of a downscaling filtering following by an up-scaling filtering. They have been assessed on both LCD and CRT displays, in HDTV 1080i format (1920×1080 in interlaced mode). Original and distorted pictures have been respectively inserted in a grey background to fit the format. It should be noticed that interlaced mode cannot be displayed on LCD, the screen thus performed a de-interlacing of the pictures in order to display them. For this experiment, the same group of observers has been used for the two displays. The group has been split in two parts: the observers of the first part have passed the test on CRT first, the observers of the second part have passed the test on LCD first. In order to study the quality differences due to display technology, we focused on the MOS obtained for the hidden reference (non distorted) pictures. Results and 95% confidence intervals are shown in Table 1

It can be observed that for still pictures the quality on LCD is globally preferred. Overall, shortcomings of CRT displays such as flickering and limited range of luminance seems to lead to a lower feeling of natural and sense of immersion. LCD is brighter, vivid and colourful and the perceived quality of still pictures is clearly higher on it (∆MOS mean = -11.4). On the whole set of 20 pictures (hidden reference and distorted ones), the linear correlation coefficient is 0.832. As expected, quality scores on both displays are correlated but not so much. This could be due to the fact that some processing artefacts are more visible on one display than on the other.

Results on moving pictures

(MP1 and MP2)

HDTV sequences (MP1)

Figure 3 shows the mean opinion scores obtained on LCD as a functions of those obtained on CRT. The linear correlation coefficient between the two sets of scores is 0.9487. It can be seen that the perceived quality is globally better on CRT than on LCD. The average value of ∆MOS over the 80 sequences is ∆MOS mean = 10.2. In order to study more precisely the impact of display without coding artefacts considerations, we can focus on the MOS of the hidden reference sequences presented in Table 2

SDTV sequences (MP2)

The SDTV sequences of the set MP2 are computed from four HDTV sequences of the set MP1 through a half-band filtering followed by a down-sampling operation by a factor of 2 (both along horizontal and vertical directions). This processing is performed on each field of the interlaced HDTV sequence. The resulting 540i sequences are an approximation of the actual SDTV whereof format is 576i, with the advantage that it does not necessitate any interpolation.

The MOS obtained on LCD are plotted as a function of the MOS obtained on CRT in Figure 4. The linear correlation coefficient between the two sets of scores is 0.9541. Here again, the perceived quality is preferred on CRT than on LCD. But whereas the gap is quite important in HD (set MP1), it's less blatant for SD materials (set MP2). The average loss of quality between CRT and LCD is almost reduced by a factor 2: ∆MOS mean = 5.9.

Conclusion

For all experiments, a high correlation exists between perceived quality on CRT and perceived quality on LCD.

Experiments with Toyama database (SP1) have highlighted a difference in the use of the quality scale between CRT (Japan) and LCD (France). This result can be due to the dif-ference of display but it's likely that the lab effect (along with cultural differences) could be responsible for an important part, even if viewing conditions in the two labs were as close as possible.

Experiments with the set SP2 have shown a significant preference for images displayed on LCD whereas experiments with the set MP1 pointed out that moving pictures were largely preferred on CRT display. These results seem to agree with previous studies [START_REF] Itu | Report on results of comparative subjective picture quality assessment test between CRT and LCD[END_REF], highlighting that LCD moving artefacts could be responsible for a loss of perceived quality on LCD. More particularly, LCD motion blur can be very annoying on sequences with a lot of movements. Experiments with SDTV materials (set MP2) have also shown a difference of perceived of quality between CRT and LCD. But this difference was less significant than for HDTV materials.

In the following, the assumption is done that the loss of quality on LCD is mainly due to motion blur. In order to verify this hypothesis, the motion blur is analysed and measured. A linear model is validated which permits to predict blur from the temporal response function of the LCD.

LCD motion blur 3.1 LCD motion blur perception

Despite recent improvements to LCD technology such as response time compensation [START_REF] Mccartney | A liquid crystal display response time compensation feature integrated into an LCD panel timing controller[END_REF], LCD motion blur remains very annoying for sequences with rapid movements. In fact, even if the response time of a liquid crystal matrix was reduced to zero, motion blur would still appear. This is due to sampleand-hold behaviour of the display : the light intensity is sustained on the screen for the duration of the frame, whereas on CRT light intensity is a pulse which fades over the frame duration (cf. Figure 5). LCD displays are so called hold-type displays. The main difference happens when the eye of the observer is tracking a moving object on the screen: for a given frame, the picture is still on the screen while the eye is still moving slightly anticipating the movement of the object. Edges of this object are displaced on the retina resulting in a blur [START_REF] Kurita | Moving picture quality improvement for hold-type AM-LCDs[END_REF].

Several authors have analysed the motion blur perception [START_REF] Kurita | Moving picture quality improvement for hold-type AM-LCDs[END_REF][START_REF] Klompenhouwer | The temporal MTF of displays and related video signal processing[END_REF][START_REF] Feng | LCD motion-blur analysis, perception, and reduction using synchronized backlight flashing[END_REF][START_REF] Li | LCD motion artifact determination using simulation methods[END_REF][START_REF] Watson | The Spatial Standard Observer: A human vision model for display inspection[END_REF]. Pan et al. [START_REF] Pan | LCD motion blur modeling and analysis[END_REF] have developed a mathematical model in which the temporal response of the liquid crystal display is a parameter. This model is designed to predict the perception of a moving edge on a LCD device. They showed that the perceived blurred edge width BEW can be expressed as a function of the velocity V of the object with the following relation:

BEW = aV (2)
where a can be obtained from the temporal response function of the LCD.

Objective motion blur measurements

Spatial measurement of a moving edge have been performed using a high-frame-rate stationary camera. The blurred edge profile is obtained from the spatial measurements by motion compensation and temporal integration [START_REF] Feng | Comparison of motion blur measurement in lcd[END_REF][START_REF] Becker | Motion blur measurement and evaluation: From theory to the laboratory[END_REF] to reproduce the smooth pursuit and the temporal integration of the eyes. This method is very similar to the famous MPRT measurement system [START_REF] Oka | Moving picture response time (MPRT) measurement system[END_REF] but it does not require a moving camera system and it's easier to carry out. The apparatus used for this measurements consisted in a high-frame-rate CCD camera and a PC used to control the camera, to store grabbed frames, and to display stimuli on the test display. A JAI PULNiX's Gigabit Ethernet CCD camera, the TM-6740GE, has been used for these measurements. It was linked to the control PC via Ethernet, using a Gigabit Ethernet Vision (GigE Vision) interface which permits to reach high frame rate. Its frame rate has been set to 1200 Hz with a resolution of 224×160 pixels. The display frequency has been set to 60 Hz, thus we obtain 20 CCD frames for each display frame. The distance between the measured display and the camera has been set in such a way that one pixel on the display array is pictured by 4×4 pixels on the CCD array. This permitted us to obtain a good approximation of the 56×40 pixels display frame by computing the mean of each 4×4 blocks in the CCD frame. Stimuli were generated with Matlab on a PC using the PsychToolbox extension [START_REF] Brainard | The Psychophysics Toolbox[END_REF]. They consisted of a straight edge moving from left to right. One example of frames grabbed by the CCD camera is shown in Figure 6. As mentioned before, the blurred profile was obtained by motion compensation of each CCD frames to simulate the smooth pursuit of the eyes. The high camera frame rate and the precise calibration of apparatus to have 4×4 CCD camera pixels to picture one display pixel permit us to achieve this motion compensation precisely. Next, all frames are added to each other to simulate the temporal integration on the retina. An example of blurred edge obtained with this method is shown in Figure 7 for a edge moving with a velocity V = 10 pixels per frame. The blurred edge width BEW (in pixels) is measured as illustrated. The blurred edge time BET (in seconds or in frames) is generally used, it's expressed by dividing BEW by the velocity V (in pixels per seconds or pixels per frame):

BET = BEW/V (3)
Moreover, it has been observed that for a given grey-to-grey transition (i.e. for a given temporal response of the liquid crystal cells), BET was not varying with the velocity V . In other terms, the measured blur width BEW was proportional to the velocity of the moving edge. This result agree with the relation 2 and the parameter a can then be identified with the blurred edge time BET .

Perceived motion blur measurements

Experiments have been designed in order to subjectively measure the perceived blur width as a function of motion speed. However, the perception of motion blur is directly related to the tracking of the moving object. If the observer stops to track the stimulus, to measure blur for example, then the blur is not perceived anymore. That's why we had to design an experiment in which the measurement of the blur is done while perceiving it. This has been done by displaying on LCD a periodical struc-7 Many grey-to-grey transitions have been tested in both horizontal and vertical directions. The length of a session varied from an observer to an other, but the average time was between 10 and 15 minutes. Seven observers participated in the experiment, each has repeated the test twice, on different days. We finally obtained a set of 14 observations for each stimulus.

An example of results is shown in Figure 9 for a given grey-to-grey transition. In the explored range of speed, the width of blur BEW was proportional to motion velocity V , as well for horizontal movement as for vertical movement. It can be observed that there were no significant differences between the two directions. For each grey-to-grey transition tested during the experiment, the same behaviour occurred and subjective results can be fitted by the linear relation 2 with a very high correlation coefficient. It was of 0.9984 for the transition illustrated in Figure 9.

Conclusion

Both objective and subjective measurements of LCD motion blur have led to a linear relation between the blurred edge width BEW and the velocity V . The proportionality factor a is constant for a given grey-to-grey transition. It has been identified to the blurred edge time BET and can be computed from the temporal response function of the LCD display [START_REF] Watson | The Spatial Standard Observer: A human vision model for display inspection[END_REF][START_REF] Feng | Comparison of motion blur measurement in lcd[END_REF]. This model enables the prediction of perceived blur for a given velocity.

With regard to this result, the fact that the loss of quality on LCD was lower with SDTV material (see section 2) can be easily explained. In SDTV, the velocity of moving objects is reduced by two in terms of resolution (pixels per second) with respect to HDTV. The magnitude of perceived motion blur is then reduced in the same proportions. The loss of quality on LCD for SDTV materials (∆MOS mean = 5.9) was around two times lower than in HDTV (∆MOS mean = 10.2). This is another evidence to the assumption that LCD motion blur is the main cause of the loss of perceived quality on LCD. Moreover, this tends to show that LCD motion blur is typically an HDTV artefact since it's less visible and then less annoying at lower resolutions.

In the following section, a objective motion-blurbased model is designed in order to predict LCD perceived quality.

Prediction model of video differences between CRT and LCD

Figure 10 illustrates the both subjective and objective way to obtain the difference of quality ∆MOS.

Since subjective assessment gives a value of ∆MOS for each sequence, an objective model is designed in order to predict this quality loss ∆MOS p from the magnitude of blur in the sequence. As shown in the previous section, the perceived blur is linearly related to the velocity of the movement.

The prediction is made in four steps. Firstly, a motion estimation is performed along the sequence. This leads to the construction of tubes which are the sets of blocks positions along the direction of motion. Each tube is then classified according to his spatial content. Secondly, tubes categorised as textures and contours are selected and an average motion vector is computed from all the vectors of these selected tubes. Thirdly, an average magnitude of motion blur is predicted using the linear model of the previous section. Fourthly, ∆MOS p is computed from a prediction model.

Motion estimation

As sequences are interlaced, motion estimation is made on each field. A block 16×8 of an odd (resp. even) field is simultaneously compared to blocks of the two previous and the two next odd (resp. even) fields (Figure 11). The position which minimises the mean square error is chosen. Thus, a vector is obtained for each 16×8 block of each group of five consecutive odd (resp.even) fields. For each group of five frames, the motion vectors of even and odd fields are then merged in order to obtain a vector for each 16×16 block. These blocks which are followed along five frames are so called spatiotemporal tubes. Each tubes are classified into categories: contours, textures or uniform areas.

Average blur index

Since motion blur is only visible with a sufficient contrast [START_REF] Laird | Spatio-velocity CSF as a function of retinal velocity using unstabilized stimuli[END_REF], tubes classified contours and textures are selected. For each group of five frames, a spatial vector is computed averaging the vectors of selected tubes. These spatial vectors are then tempo- rally averaged along the sequence. A global motion vector is obtained for each sequence. The norm V of this global vector is finally used to compute the width of perceived motion blur according to previous section (cf. Equation 2). The obtained BEW is an indicator of the magnitude of perceived blur along the sequence.

Prediction

A indicator of the quantity of perceived motion blur has been computed for each sequence. The main objective of this work is to determine the relation between the LCD motion blur and the loss of quality observed between CRT and LCD displays. A non linear function ∆MOS p = f (BEW ) has been constructed in order to predict the quality difference ∆MOS from the average blur quantity BEW . It has been assumed that this function has the following shape:

• In the first part (BEW ≤ B V ), the magnitude of perceived motion blur is too small to be noticeable and to influence quality perception.

• In the second one, the quality loss increases with magnitude of motion blur.

• Finally, in the third part, the quality difference saturates despite the increase of perceived blur (BEW ≥ B S ). This saturation may be due to contextual effects such as limited assessment scale and presence of quite distorted sequences during quality assessment.

The prediction model ∆MOS p = f (BEW ) is represented in Figure 12. This model can be used to predict the quality loss ∆MOS between CRT and LCD from the average magnitude of blur measured on a sequence.

Results

The whole objective model presented in Figure 10 enables the prediction of the difference of perceived quality between CRT and LCD. This difference depends on the average motion blur measured on a sequence. This prediction of the loss of quality on LCD has sense when the motion blur is visible, i.e. for sequences with significant movements, and for video material with a high resolution. An estimation of the subjective quality scores on LCD from the subjective quality scores on CRT can be made using the following relation:

MOS est LCD = MOS CRT -∆MOS p (4)
This model is based on motion blur measurements, it can then be adapted to all types of temporal response. As a consequence, it permits to evaluate, on natural contents, the technical solutions to reduce motion blur such as back-light flashing or black data insertion.

Conclusion

In this paper, the impact of display on subjective quality assessment has been addressed. Subjective quality assessment experiments have been performed on both LCD and CRT displays. Two sets of still images and two sets of moving pictures have been assessed using either an ACR or a SAMVIQ protocol. Results have shown that CRT was globally preferred for moving pictures whereas it was the inverse for still images. As a consequence, the assumption has been done that the difference between display was mainly due to LCD moving artefacts such as motion blur. It should also be noticed that the difference of quality is reduced by a factor two for SDTV materials with respect to HDTV materials.

LCD motion blur has been then measured objectively and with psycho-physics experiments. It has been shown that the perceived blur width was varying linearly with the velocity. The parameter of this relation could be deduced from the temporal characteristics of the LCD display. This could explain the reduction of ∆MOS between HDTV and SDTV materials.

Finally, a objective model has been designed which permits to predict the differences of perceived quality between CRT and LCD. This motion-blur-based model enables the estimation of perceived quality on LCD with respect to the perceived quality on CRT. Technical solutions to LCD motion blur such as back-light flashing or black data insertion can thus be evaluated on natural contents by this mean.

This study highlighted the fact that the recent LCD technology could lead to new visual artefacts when displaying moving pictures. These new artefacts are not significant at low resolutions, which validate the use of LCD for subjective video quality assessment in the Multimedia Test plan of the VQEG [START_REF] Vqeg | Multimedia group test plan[END_REF]. However, when increasing the resolution, they become more annoying and have an important impact on the perceived quality. It comes that the subjective video quality assessment at a high resolution (HDTV for example) should be led very carefully on LCD since a significant part of the perceived distortions could be due to the display. Liquid crystal displays have to be improved in order to reach the visual quality of CRT technology, particularly in a HDTV context. However, more reduced video applications supports such as laptop, cell phone, personal digital assistants, etc. are not as much affected due to their smaller display resolutions.

Figure 1 :

 1 Figure 1: Comparison of MOS obtained on LCD (IRCCyN lab) and on CRT (University of Toyama lab) for the Toyama JPEG database.

Figure 2 :

 2 Figure 2: Comparison of MOS obtained on LCD (IRCCyN lab) and on CRT (University of Toyama lab) for the Toyama JPEG2000 database.

Figure 3 :Figure 4 :

 34 Figure 3: MOS obtained of LCD as a function of MOS obtained on CRT for the set MP1.

Figure 5 :

 5 Figure 5: Temporal evolution of a pixel's intensity on a CRT display (a), on an LCD display (b). (from Pan et al. [6])

Figure 6 :

 6 Figure 6: CCD camera frames (224×160 pixels) taken during one period of the display frame rate.

Figure 7 :

 7 Figure7: Blurred edge picture obtained after motion compensation of the camera frames and temporal summation (a), and blurred edge profile extract from the latter (b). Measurements have been performed for a black-to-white edge moving from left to right with a velocity of V = 10 pixels per frame.

Figure 8 :

 8 Figure 8: Displayed (a) and perceived (b) stimulus for an horizontal movement from left to right.

Figure 9 :

 9 Figure 9: Perceived blur width BEW as a function of motion velocity V, for a horizontal direction (filled circles) and for a vertical direction (open circles), with confidence intervals over seven observers.

Figure 10 :

 10 Figure 10: Diagram illustrating the objective prediction of the loss of quality ∆MOS p .

Figure 11 :

 11 Figure 11: Construction of the spatio-temporal tubes.

Figure 12 :

 12 Figure 12: Prediction of the loss of quality ∆MOS p from the average blur magnitude BEW .

  . The difference of perceived quality ∆MOS is computed as the difference between the scores obtained on CRT (MOS CRT ) and those obtained on LCD (MOS LCD ):

	Pictures	MOS CRT	MOS LCD	∆MOS
	FOOTBALL	70.6 (7.4)	79.7 (7.3)	-9.1
	HAND	75.8 (7.4)	79.3 (7.2)	-3.5
	HOUSE	59.8 (11.1) 80.3 (11.1)	-20.5
	LANDSCAPE 75.8 (5.8)	75.7 (5.4)	0.1
	MAP	59.8 (10.9) 83.9 (10.7)	-24.1
	Table 1: Mean opinion scores and 95% confidence
	intervals (between brackets) obtained on both dis-
	plays for the set SP2 displayed in 1080i.
	∆MOS = MOS CRT -MOS LCD

Table 2 :

 2 Mean opinion scores and 95% confidence intervals (between brackets) obtained on both displays for the set MP1.

	. It's interesting to notice that this loss of
	quality is quite important for sequences with quick
	movements such as Show, Concert, Foot and Voile.
	This loss of quality on LCD seems to be related to
	the quantity and/or the fastness of the movements
	in the sequence.

Acknowledgements

This study is supported by a student grant from the French région Pays de la Loire. Work presented in section 3.2 has been supported by TCO Development, Sweden. The authors would like to thank Arnaud Tirel and Romuald Pépion for their assistance in performing the experiments described in the paper.


Sylvain Tourancheau
email: sylvain.tourancheau@univ-nantes.fr

Kjell Brunnström
email: kjell.brunnstrom@acreo.se

Borje Andrén

Patrick Le Callet

P-92: Motion Blur Estimation on LCDs

Today there is no requirement concerning LCD motion blur in TCO Requirements ( e.g. TCO'06 Media Displays). The primary goal of this study is to find a measurement method for motion blur which is easy to carry out and gives reproducible results lab to lab. This method should be able to take in account motion blur reduction systems such as backlight flashing. Measurement results and method proposal are given.

6.

Introduction

Despite recent improvements to LCD technology such as response time compensation, LCD motion blur remains very annoying for sequences with rapid movements. In fact, even if the response time of a liquid crystal matrix was reduced to zero, motion blur would still appear. This is due to sample-and-hold behaviour of the display; the light intensity is sustained on the screen for the duration of the frame, whereas on a CRT light intensity is a pulse which fades over the frame duration. LCD displays are so called hold-type displays. The main difference happens when the eyes of the observer are tracking a moving object on the screen; for a given frame, the picture is still on the screen while the eye is still moving slightly anticipating the movement of the object. Edges of this object are integrated on the retina while moving, resulting in a blur [START_REF] Kurita | Moving picture quality improvement for holdtype AM-LCDs[END_REF].

The most common metric to characterize LCD motion blur is the MPRT (motion picture response time) [START_REF] Oka | Moving picture response time (MPRT) measurement system[END_REF] and its relative indexes BET and BEW 1 . A lot of measurement systems have been developed in order to measure MPRT [START_REF] Becker | Motion blur measurement and evaluation: From theory to the laboratory[END_REF], but they are generally quite expensive and complicated to carry out. As a consequence, alternative approaches have been proposed, based on the theoretical analysis of the display spatial and temporal apertures. It has been shown that MPRT can be obtained from the temporal impulse response [4][5] or from the temporal step response [6][7] instead of measuring the blur width spatially. First comparisons between the results of simulation methods and those of measurement systems showed that both approaches are very close [3][8]. However, these investigations only concerned few displays. Further measurements should be done in order to analyse the difference between the results of these two approaches.

Today there is no requirement concerning LCD motion blur in TCO'06 Displays [START_REF] Tco Development | TCO'06 Media Displays[END_REF] and the requirements concerning response time is not sufficient to guarantee a low level of motion blur annoyance. In particular, display devices with impulsive driving techniques to reduce motion blur such as backlight flashing (BF) and black data insertion (BDI) have difficulties to obtain the TCO label concerning response time. Actually, it's difficult to measure response time in a classical way when these motion-blur reduction techniques are present. The primary goal of this study is to find a 1 respectively blurred edge time, blurred edge width. measurement method of motion blur which is easy to carry out and which can be reproduce from one lab to another assuming the less variations possible of the results. For these reasons, measurement systems such as those using tracking camera are of less interest. Moreover, it could be interesting to relate this motion blur measurement to response time measurements already obtained for TCO'06 Displays, simulation approaches such as the use of temporal step-responses could be useful, especially concerning display with impulse driving techniques.

In this paper, both methods have been carried out. Five displays have been tested and results from both spatial and temporal measurements are compared and discussed.

Measurements

Two types of measurements have been studied in this paper. Some spatial measurement of a moving edge have been performed using a high-framerate stationary camera. We also used a number of temporal step-responses, obtained from TCO'06 response-time requirements measurements [START_REF] Tco Development | TCO'06 Media Displays[END_REF]. The blurred edge profile is obtained from the spatial measurements by motion compensation and temporal integration [3][8]. It can also be deduced from the step-response by convolution with a window having a width of one frame-period [START_REF] Klompenhouwer | Temporal impulse response and bandwidth of displays in relation to motion blur[END_REF]

[7][3][8].

Spatial measurements of a moving edge

The apparatus used for the experimental measurements consisted in a high-framerate CCD camera and a PC used to control the camera, to store grabbed frames, and to display stimuli on the test display. A JAI PULNiX's Gigabit Ethernet CCD camera, the TM-6740GE, has been used for these measurements. It was linked to the control PC via Ethernet, using a Gigabit Ethernet Vision (GigE Vision) interface which permits to reach high framerate. Its framerate has been set to 1200 Hz with a resolution of 224x160 pixels. The display framerate has been set to 60 Hz, thus we obtain 20 CCD frames for each display frame. The distance between the measured display and the camera has been set precisely in such a way that one pixel on the display array is pictured by 4x4 pixels on the CCD array. This permitted us to obtain a good approximation of the 56x40 pixels of the display by computing the mean of each 4x4 blocks in the CCD frame. One example of frames grabbed by the camera is shown in Figure 1.

Stimuli were generated with Matlab on a PC using the PsychToolbox extension [START_REF] Brainard | The Psychophysics Toolbox[END_REF]. They consist of a straight edge moving from left to right. Three values can be set: the start gray level value Ns which is the gray level of the right part of the screen, the final gray level value Nf which is the gray level of the moving part, on the left of the screen, and the velocity V in pixels per frame. Five gray levels have been used in the measurements: 0, 63, 127, 191 and 255. Thus, 20 transitions have been studied.

As mentioned before, the blurred profile was obtained by motion compensation of each CCD frames to simulate the smooth pursuit of the eyes. The high camera framerate and the precise calibration of apparatus to have 4x4 CCD camera pixels to picture one display pixel permit us to achieve this motion compensation precisely. Next, all frames are added to each other to simulate the temporal integration on the retina. An example of blurred edge obtained with this method is shown in Figure 2a for a edge moving with a velocity V = 10 pixels per frame. The blurred edge width BEW (in pixels) is computed as illustrated. The normalized blurred edge width NBEW can be expressed by dividing BEW by the velocity V (in pixels per frame): NBEW = BEW/V.

Temporal step-response measurements

The sensor of the response time measurement system consists of a fast photo diode connected to an oscilloscope. The diode is housed in a special purpose box with an opening of 2 mm by 2 mm surrounded by black velvet. The sensor box is attached directly on the screen surface. The temporal response of the displays is sampled with a sample time of 0.1 ms.

For these measurements, the stimulus consisted in a set of 20 transitions from one gray level to another among five. Each gray patch is displayed during 20 frames. The following gray levels (DAC values) have been used: 0, 63, 127, 191, 255. We obtained thus 20 step-responses, 10 rise responses (when the start level is lower than the final level) and 10 fall responses (when the start level is higher than the final level).

Example of the temporal step-response for a transition from 0 to 255 is shown in Figure 2b. The response time value τ is computed from this step-response according to recommendations [START_REF] Vesa | Flat Panel Display Measurements[END_REF] between 10% and 90% luminance level. The blurred edge profile can then be obtained by convolving the temporal step-response with a window which has a width of one frame-period. For an edge moving from left to right with a start value Ns and a final value Nf as defined previously, the pixels before the edge will change from Ns to Nf . As a consequence, their temporal response corresponds to the step-response from Ns to Nf but it has to be flipped horizontally in order to match with the blurred profile as it appears on the display [START_REF] Feng | Comparison of motion blur measurement in LCD[END_REF]. The blurred edge profile corresponding to the temporal step response of Figure 2b is shown in Figure 2c.

The blurred edge time BET (in seconds) is obtained as illustrated.

The normalized blurred edge time NBET is expressed in frames by dividing BET by the frame period T (in seconds): NBET = BET/T .

As a result, both NBEW and NBET values are expressed in frames. This permits to compare both methods of measurement.

Results

Five very recent monitor displays have been tested in this work. They were all AM-TFT LCD with a refresh frequency of 60 Hz, with different types of panel, sizes and resolutions as depicted in Table 1. In the following, they are identified with letters from A to E. Both B and C were equipped with backlight flashing (BF). The response time (RT) given by the manufacturers is also mentioned. Despite of having different types of TFT panels, the five displays are said to have almost similar grey-to-grey response times.

For each display, and for each transition, the normalised blur width has been computed with both approach: from the spatial measurements (called NBEW in the following) and from the temporal step-response measurements (called NBET in the following). The VESA response time [START_REF] Vesa | Flat Panel Display Measurements[END_REF] has been computed as well from the temporal step-response measurements. Mean values (across the 20 transitions) of response time (τ), NBEW and NBET are presented in Table 2 for the five displays under test. The root mean square error (RMSE) between NBEW and NBET values has been computed as well. We can first observe that the values of grey-to-grey response times are very far from those given by manufacturers.

As expected, NBET values for displays with backlight flashing (B and C) are the lowest although their response time are quite high. Figure 3 gives the response times and the NBET values of displays A and B for each transition. It can be observed that both histograms are very similar for display A which do not have backlight flashing. The linear correlation coefficient between both sets of values is 0.977. On the other hand, for display B, with backlight flashing, both quantities are less correlated (-0.762). Moreover, display A has a response time which is 28% lower than the one of display B, whereas the motion blur width on it will be 30% higher than on display B.

Discussion

Results obtained from the two different approaches on a display with backlight flashing (display C) are compared in Figure 4. The blurred edge profiles shown in Figures 4b and4c are plotted together in Figure 4d, as a function of time (by scaling the space domain with the velocity V [START_REF] Watson | The Spatial Standard Observer: A human vision model for display inspection[END_REF]). It can be noticed that both spatial and temporal measurements of the blurred edge profile give some clean results on which it is easy to compute blur width values, and this despite of the high frequency modulation on the temporal response (Figure 4a) due to backlight flashing. All these measurements confirm that spatial blurred edge profile can be easily drawn from some classical temporal step-response measurements [7][8]. Even for LCD displays with impulse-type improvements such as backlight flashing.

However, although NBET and NBEW should theoretically be the same, some differences occur between both methods of measurement (Table 2). For displays B and C, these differences are not so important (RMSE below 0.1) despite of the temporal modulations due to backlight flashing. For the three first displays NBEW is always slightly larger than NBET (for each of the 20 transitions), which is the opposite result of the one reported by Feng et al. [START_REF] Feng | Comparison of motion blur measurement in LCD[END_REF]. On the other hand, for displays D and E, NBEW is globally lower to NBET (depending on the transition) and the difference is quite high, especially for display D.

Globally, the differences could be explained by measurement errors, particularly concerning the spatial measurements. Grabbed frames could have been quite noisy especially for low luminance transitions (the more significant differences between NBET and NBEW occur for transitions between low levels of luminance). Temporal measurements may be considered more accurate because they are generally sampled with a higher sample rate and they do not require any image processing or motion compensation. Moreover, temporal measurements are easier to carry out and reproducible from one lab to another. However, some display improvements with spatial variations such as black data insertion, scrolling backlight or for example areaadaptive LED backlight would not be taken in account with purely temporal measurements.

Conclusion

In this paper, we presented some results of motion blur measurements on LCD displays. Two methods have been used to obtain blurred edge profiles. First one used a stationary camera to picture the moving edge. Second one consists in the convolution of a one-frame-period large window with the temporal stepresponse of the display. Measured blur indexes are compared between them and with the response time. Both approaches give quite similar results, even for displays with impulse-type motion blur reduction such as backlight flashing, but temporal measurements are easier to perform and seem to be more reliable.

5.

Figure 2 -

 2 Figure 2 -Illustration of measurement performed for a transition from Ns = 0 to Nf = 255: blurred edge profile obtained by spatial measurement of an edge moving at V = 10 pixels per frame (a), temporal step-response (b) and blurred edge profile obtained from the step-response (c).

Figure 1 -

 1 Figure 1 -CCD camera frames (224x160 pixels) taken during one display refresh period.

Figure 3 -

 3 Figure 3 -VESA response times (a) and NBET (c) of display A for each transition, idem for display B (b) and (d).

Figure 4 -

 4 Figure 4 -Results of measurements on display C (with backlight flashing), temporal step-response (a), blurred edge profile from temporal response (b), blurred edge profile from spatial measurements (c), comparison of both methods (d).

Table 1 -Specifications of tested displays

 1

	Id Type Size Resolution RT (ms)	Notes
	A IPS 20'' 1600x1200	8
	B IPS 26'' 1920x1200	5	Backlight flashing
	C IPS 30'' 2560x1600	5	Backlight flashing
	D VA 22'' 1680x1050	8
	E TN 24'' 1920x1200	5

Table 2 -Results of measurements (mean values)

 2

	Id	τ (ms)	NBEW	NBET	RMSE
	A	12.7	1.19	1.12	0.112
	B	17.6	0.86	0.86	0.034
	C	14.3	0.90	0.89	0.042
	D	19.5	1.01	1.39	0.519
	E	20.4	1.28	1.39	0.143

Acknowledgement

This work has been financed by TCO Development and VINNOVA (The Swedish Governmental Agency for Innovation Systems), which is hereby gratefully acknowledged. It has also been supported by the French région Pays de la Loire.

The authors would also like to thank Intertek Semko Sweden for their assistance in the study.