Impact of Fixation Time on Subjective Video Quality Metric: a New Proposal for Lossy Compression Impairment Assessment
In this paper, a new approach for quality assessment
tasks in lossy compressed digital video is proposed. The research
activity is based on the visual fixation data recorded by an eye
tracker. The method involved both a new paradigm for subjective
quality evaluation and the subsequent statistical analysis to match
subjective scores provided by the observer to the data obtained from
the eye tracker experiments. The study brings improvements to the
state of the art, as it solves some problems highlighted in literature.
The experiments prove that data obtained from an eye tracker can be
used to classify videos according to the level of impairment due to
compression. The paper presents the methodology, the experimental
results and their interpretation. Conclusions suggest that the eye
tracker can be useful in quality assessment, if data are collected and
analyzed in a proper way.
[1] F. Lukas Z. Budrikis, Picture Quality Prediction Based on a Visual
Model, IEEE Transaction on Communications, vol. 30, issue 7, 1982,
pp. 1679-1691.
[2] N. Nill, A Visual Model Weighted Cosine Transform for Image
Compression and Quality Assessment, IEEE Transaction on
Communications, vol 33 Issue 6, 1985, pp. 551-557.
[3] R. Amadeo. Compressed Video Quality Assessment: from Subjective to
Objective Metrics, Master thesis, University of Pavia, Engineering
Faculty, 2011.
[4] U. Reiter, J. Korhonen, Comparing apples and oranges: subjective
quality assessment of streamed video with different types of distortion,
Proc. Of International Workshop on Quality of Multimedia Experience,
2009, pp. 127-132.
[5] Y. Ou, Y. Zhou, Y.Wang, Perceptual quality of video with frame rate
variation: a subjective study, 2010, Proc. Of Acoustics Speech and
Signal Processing (ICASSP), pp- 2446-2449.
[6] A. K. Moorthy, K. Seshadrinathan, R. Soundararajan and A. C. Bovik,
"Wireless video quality assessment: A study of subjective scores and
objective algorithms". IEEE Transactions on Circuits and Systems for
Video Technology, vol. 20, no.4, April 2010, pp. 513-516.
[7] 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.
[8] H. R. Wu, K. R- Rao: Digital Video Image Quality and Perceptual
Coding, Taylor and Francis ed. 2006, pp. 125-151.
[9] http://trace.eas.asu.edu/yuv/ (Video trace library of Arizona State
University)
[10] ftp://ftp.tnt.uni-hannover.de/pub/svc/testsequences/ (Hannover Liebnitz
University video library)
[11] http://media.xiph.org/video/derf/
[12] User Manual - Tobii Eye Tracker, Clearview analysis software -
February 2006
[13] T. Tominaga, T. Hayashi, J. Okamoto, A. Takahashi, Performance
comparisons of subjective quality assessment methods for mobile video,
Proc. Of Quality of Multimedia Experience (QoMEX), 2010pp. 82-87.
[1] F. Lukas Z. Budrikis, Picture Quality Prediction Based on a Visual
Model, IEEE Transaction on Communications, vol. 30, issue 7, 1982,
pp. 1679-1691.
[2] N. Nill, A Visual Model Weighted Cosine Transform for Image
Compression and Quality Assessment, IEEE Transaction on
Communications, vol 33 Issue 6, 1985, pp. 551-557.
[3] R. Amadeo. Compressed Video Quality Assessment: from Subjective to
Objective Metrics, Master thesis, University of Pavia, Engineering
Faculty, 2011.
[4] U. Reiter, J. Korhonen, Comparing apples and oranges: subjective
quality assessment of streamed video with different types of distortion,
Proc. Of International Workshop on Quality of Multimedia Experience,
2009, pp. 127-132.
[5] Y. Ou, Y. Zhou, Y.Wang, Perceptual quality of video with frame rate
variation: a subjective study, 2010, Proc. Of Acoustics Speech and
Signal Processing (ICASSP), pp- 2446-2449.
[6] A. K. Moorthy, K. Seshadrinathan, R. Soundararajan and A. C. Bovik,
"Wireless video quality assessment: A study of subjective scores and
objective algorithms". IEEE Transactions on Circuits and Systems for
Video Technology, vol. 20, no.4, April 2010, pp. 513-516.
[7] 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.
[8] H. R. Wu, K. R- Rao: Digital Video Image Quality and Perceptual
Coding, Taylor and Francis ed. 2006, pp. 125-151.
[9] http://trace.eas.asu.edu/yuv/ (Video trace library of Arizona State
University)
[10] ftp://ftp.tnt.uni-hannover.de/pub/svc/testsequences/ (Hannover Liebnitz
University video library)
[11] http://media.xiph.org/video/derf/
[12] User Manual - Tobii Eye Tracker, Clearview analysis software -
February 2006
[13] T. Tominaga, T. Hayashi, J. Okamoto, A. Takahashi, Performance
comparisons of subjective quality assessment methods for mobile video,
Proc. Of Quality of Multimedia Experience (QoMEX), 2010pp. 82-87.
@article{"International Journal of Information, Control and Computer Sciences:54437", author = "M. G. Albanesi and R. Amadeo", title = "Impact of Fixation Time on Subjective Video Quality Metric: a New Proposal for Lossy Compression Impairment Assessment", abstract = "In this paper, a new approach for quality assessment
tasks in lossy compressed digital video is proposed. The research
activity is based on the visual fixation data recorded by an eye
tracker. The method involved both a new paradigm for subjective
quality evaluation and the subsequent statistical analysis to match
subjective scores provided by the observer to the data obtained from
the eye tracker experiments. The study brings improvements to the
state of the art, as it solves some problems highlighted in literature.
The experiments prove that data obtained from an eye tracker can be
used to classify videos according to the level of impairment due to
compression. The paper presents the methodology, the experimental
results and their interpretation. Conclusions suggest that the eye
tracker can be useful in quality assessment, if data are collected and
analyzed in a proper way.", keywords = "eye tracker, video compression, video qualityassessment, visual attention", volume = "5", number = "11", pages = "1271-8", }