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Maria Grazia Albanesi, Riccardo Amadeo

Impact of Fixation Time on Subjective Video Quality Metric: a New Proposal for Lossy Compression Impairment Assessment. Maria Grazia Albanesi, Riccardo Amadeo University of Pavia, Faculty of Engineering , Computer Department.

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Maria Grazia Albanesi, Riccardo Amadeo

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  1. Impact of Fixation Time on Subjective Video Quality Metric: a New Proposal for Lossy Compression Impairment Assessment Maria Grazia Albanesi, Riccardo Amadeo University of Pavia, Faculty of Engineering, Computer Department ICMVIPPA 2011 : International Conference on Machine Vision, Image Processing, and Pattern Analysis Venezia (Mestre), November 28, 2011

  2. Outline • The addressed problem: • subjective video quality assessment for lossy compression impairment • The tools and the experiments • eye tracking and subjective experiments • The goals • Comparison to literature • The results and their interpretation • A possible application: a new protocol for no-reference video quality assessment • Future developments ICMVIPPA 2011 - Venezia (Mestre) - November 28, 2011

  3. The problem How I can measure the loss of quality due to compression? • Field of applications: TV, video services on Internet, video for mobile applications, test of emerging compression algorithms….. Evaluation of multimedia quality user experience • Two approaches: objective and subjectivemetrics • Our goal: find objective parameters coming form subjective experiments which reflect the subjective video quality, as perceived by a human observer. ICMVIPPA 2011 - Venezia (Mestre) - November 28, 2011

  4. The tools: eye tracker and subjective QA experiments • Eye tracker: it records the point and the duration of fixation of the eye, when the observer looks at a monitor. • Data are subsequently analyzed from a statistical point of view (mean, std. dev….) ICMVIPPA 2011 - Venezia (Mestre) - November 28, 2011

  5. The set of videos • A set of 19 videos downloaded from available online public libraries • http://trace.eas.asu.edu/yuv/ (Video trace library of Arizona State University) • ftp://ftp.tnt.uni-hannover.de/pub/svc/testsequences/ (Hannover Liebnitz University video library) • http://media.xiph.org/video/derf/ • The original files: YUV sequences, 4:2:0, in CIF resolution (352x288) at 30 fps are converted in avi sequences and compressed by a H.264 at 2 bitrates: 450 bps and 150 bps ICMVIPPA 2011 - Venezia (Mestre) - November 28, 2011

  6. Examples: ICMVIPPA 2011 - Venezia (Mestre) - November 28, 2011

  7. Visual behavior and impairment Visual path for original «best» video Visual path for compressed (br150 bps) video

  8. Methodology • Protocol ACR5-HR (absolutelycategory ranking – hiddenreference • MOS scale with fivelevels: • Onlyoneobservation for each video • The observerhas no information about the unimpairedversion of the video. • The subjects: 8 females and 10 males, of age varying from 22 to 27 years old. • Their vision was normal or corrected-to-normal • They had no experience in subjective video quality assessment. • They had normal or good experience in using IT interfaces to watch videos both online and offline. ICMVIPPA 2011 - Venezia (Mestre) - November 28, 2011

  9. Novelties and comparison to literature • Our parameter are not related to fixation points, but to the duration of the fixation. • Videos are classified according to color content relevance and and movement relevance to create semantic filters • Parameters: • Duration of fixation time • MOS, five point scale • Subjective Color Score, three point scale • Subjective Movement Score (SCS e SMS), three point scale. • Removal of «Memory effect» due to the conditioning of ocular motion activity by the visual attention of preceding scenes. • Startingpoint: O. Le Meur, A. Ninassi, P. Le Callet, D. Barba, Overt visual attention for free-viewing and quality assessment tasks: Impact of the regions of interest on a video quality metric, Signal Processing Image Communication, 2010, vo. 25, pp- 547-548.

  10. Playlists to removememoryeffect • 18 tester, 6 in eachplaylist • Each video hasthreeversion: reference br450 br150 (57 videos) • Eachobserverlooksatonlyoneversion of each video. No repetitions are allowed in eachplaylists. ICMVIPPA 2011 - Venezia (Mestre) - November 28, 2011

  11. Mean Opinion Score The MOS reallyreflect the progressive loss of quality due to compression.

  12. SCS e SMS • Color and movement are consideredrelevantif the score is > 2 • «Highly animated video»: 2, 4, 7, 8, 10, 12, 14, 17, 19 • «Highly coloured video» »: 3, 5, 7, 10, 15, 19

  13. Analysis of Meanfixation time (MFT) • The meanfixation time doesnotseem to be related to the video quality!

  14. MFT, semanticfiltering • Even by filtering by movement or colour, thereisnot a clear relation betweenMFT and MOS

  15. Analysis of Standard deviation of FT • Even the standard deviation of fixationtime doesnotseem to be related to the video quality!

  16. The solution: thirdorderstatistics! • Standard deviation of FT ison the averageless for videos of high quality • The semanticfiltering shows thatthisbehaviourisstressened for highlyanimatedvideos.

  17. Conclusions and future researches • The duration of fixation time seems to have a more predictable behavior when the observer watches to a high quality video. • If we compute the third order statistics on the fixation time, we can guess a rank of a collection of video which reflects the perceptive visual quality • The experiments confirm this behavior for degradation due to lossy compression. • The rank according third order statistic reflect the loss of quality and subjective MOS especially for highly animated videos. Future researches: • Test on a greater level of quality impairments • Test on other kinds of quality impairments • Finding a more efficient semantic filtering about color or other criteria. ICMVIPPA 2011 - Venezia (Mestre) - November 28, 2011

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