A New Categorization of Image Quality Metrics Based On a Model of Human Quality Perception
Authors: Maria Grazia Albanesi, Riccardo Amadeo
Abstract:
This study presents a new model of the human image quality assessment process: the aim is to highlightthe foundations of the image quality metrics proposed in literature, by identifyingthe cognitive/physiological or mathematical principles of their development and the relation with the actual human quality assessment process. The model allows to createa novel categorization of objective and subjective image quality metrics. Our work includes an overview of the most used or effectiveobjective metrics in literature, and, for each of them, we underline its main characteristics, with reference to the rationale of the proposed model and categorization. From the results of this operation, we underline a problem that affects all the presented metrics: the fact that many aspects of human biasesare not taken in account at all. We then propose a possible methodology to address this issue.
Keywords: Eye-Tracking, image quality assessment metric, MOS, quality of user experience, visual perception.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1093223
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2455References:
[1] M. Fiedler, T. Hossfeld and P. Tran-Gia, "A generic quantitative relationship between quality of experience and quality of service," IEEE Network, vol. 24, no. 2, pp. 36-41, 2012.
[2] W. Lin and J. C.-C. Kuo, "Perceptual visual quality metrics: A survey," Journal of Visual Communication and Image Representation, vol. 22, no. 4, pp. 297-312, 2011.
[3] J. You, U. Reiter, M. Hannuksela, M. Gabbouj and A. Perkis, "Perceptual-based quality assessment for audio–visual services: A survey," Signal Processing: Image Communication, vol. 25, no. 7, pp. 482-501, 2010.
[4] I.-T. P.910, "Subjective video quality assessment methods for multimedia applications," ITU’s Telecommunication Standardization Sector, 04/2008.
[5] I.-T. P.911, "Subjective audiovisual quality assessment methods for multimedia applications," ITU’s Telecommunication Standardization Sector, 12/1998.
[6] I.-T. P.913, "Methods for subjectively assessing audiovisual quality of internet video and distribution quality television, including separate assessment of video quality and audio quality, and including multiple environments," ITU’s Telecommunication Standardization Sector, 01/2014.
[7] T. Tominaga, T. Hayashi, J. Okamoto and A. Takahashi, "Performance comparisons of subjective quality assessment methods for mobile video," in Second International Workshop on Quality of Multimedia Experience (QoMEX), Trondheim, 2010.
[8] P. Corriveau, C. Gojmerac, B. Hughes and L. Stelmach, "All subjective scales are not created equal: The effects of context on different scales," Signal Processing, vol. 77, no. 1, pp. 1-9, 1999.
[9] W. R. Hendee and P. N. T. Wells, The Perception of Visual Information, New York: Springer, 1997.
[10] O. Le Meur, P. Le Callet, D. Barba and D. Thoreau, "A coherent computational approach to model bottom-up visual attention," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 5, pp. 802-817, 2006.
[11] TAMPERE IMAGE DATABASE 2008 TID2008, "TID2008 database," Institute of Signal Processing, Tampere University of Technology, 22 02 2010.
[Online]. Available: http://www.ponomarenko.info/tid2008.htm.
[Accessed 15 1 2014].
[12] S. Winkler and P. Mohandas, "The Evolution of Video Quality Measurement: From PSNR to Hybrid Metrics," IEEE Transactions on Broadcasting, vol. 54, no. 3, pp. 660-668, 2008.
[13] W. Zhou and A. C. Bovik, "Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures," IEEE Signal Processing Magazine, vol. 26, no. 1, pp. 98-117, 2009.
[14] N. Damera-Venkata, T. D. Kite, W. S. Geisler, B. L. Evans and A. C. Bovik, "Image Quality Assessment on a Degradation Model," IEEE Transactions on Image Processing, vol. 9, no. 4, pp. 636-649, 2000.
[15] E. Peli, "Contrast in Complex Images," Journal of the Optical Society of America A, vol. 7, no. 10, pp. 2032-2040, 1990.
[16] Z. Whang and A. C. Bovik, "A Universal Image Quality Index," IEEE Signal Processing Letters, vol. XX, no. Y, pp. 1-4, 2002.
[17] H. R. Sheikh, A. C. Bovik and G. de Veciana, "An Information Fidelity Criterion for Image Quality Assessment Using Natural Scene Statistics," IEEE Transactions on Image Processing, vol. 14, no. 12, pp. 2117-2128, 2005.
[18] H. R. Sheikh and A. C. Bovik, "Image Information and Visual Quality," IEEE Transactions on Image Processing, vol. 15, no. 2, pp. 430-444, 2006.
[19] Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, "Image Quality Assessment: From Error Visibility to Structural Similarity," IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, 2004.
[20] Z. Wang, E. P. Simoncelli and A. C. Bovik, "Multi-Scale Structural Similarity for Image Quality Assessment," in Proceedings of the 37th IEEE Asiloma Conference on Signal, Systems and Computers, Pacific Grove, CA, 2003.
[21] L. Zhang, L. Zhang, X. Mou and D. Zhang, "FSIM: A Feature Similarity Index for Image Quality Assessment," IEEE Transactions on Image Processing, vol. 20, no. 8, pp. 2378-2386, 2011.
[22] P. Dostal, L. Krasula and M. Klima, "HLFSIM: Objective Image Quality Metric Based on ROI Analysis," in IEEE International Carnahan Conference on Security Technology (ICCST), Boston, MA, 2012.
[23] K. Egiazarian, J. Astola, N. Ponomarenko, V. Lukin, F. Battisti and M. Carli, "Two New Full-Reference Quality Metrics Based on HVS," in Proceedings of the Second International Workshop on Video Processing and Quality Metrics, VPQM, vol. 4, Chandler, AZ, 2006.
[24] N. Ponomarenko, F. Silvestri, K. Egiazarian, M. Carli, J. Astola and V. Lukin, "On Between-Coefficient Contrast Masking of DCT Basis Functions," in Third International Workshop on Video Processing and Quality Metrics (VPQM), Scottsdale, AZ, 2007.
[25] N. Ponomarenko, O. Ieremeiev, V. Lukin, K. Egiazarian and M. Carli, "Modified image visual quality metrics for contrast change and mean shift accounting," in 11th International Conference The Experience of Designing and Application of CAD Systems in Microelectronics (CADSM), Polyana-Svalyava, 2011.
[26] D. M. Chandler and S. Hemami, "VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images," IEEE Transactions on Image Processing, vol. 16, no. 9, pp. 2284-2298, 2007.
[27] A. Mittal, A. K. Moorthy and A. C. Bovik, "No-Reference Image Quality Assessment in the Spatial Domain," IEEE Transactions on Image Processing, vol. 21, no. 12, pp. 4695-4708, 2012.
[28] A. Mittal, R. Soundararajan and A. C. Bovik, "Making a ‘Completely Blind’ Image Quality Analyzer," IEEE Signal Processing Letters, vol. 20, no. 3, pp. 209-212, 2013.
[29] A. K. Moorthy and A. C. Bovik, "Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality," IEEE Transactions on Image Processing, vol. 20, no. 12, pp. 3350-3364, 2011.
[30] M. A. Saad, A. C. Bovik and C. Charrier, "Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain," IEEE Transactions on Image Processing, vol. 21, no. 8, pp. 3339-3352, 2012.
[31] E. Karapanos, J.-B. Martens and M. Hassenzahl, "Accounting for diversity in subjective judgments," in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Chicago, IL, 2009.
[32] T. Hoßfeld and S. E. Raimund Schatz, "SOS: The MOS is not enough," in Third International Workshop on Quality of Multimedia Experience, Mechelen, 2011.
[33] M. C. Morrone and R. A. Ownes, "Feature detection from local energy," Pattern Recognition Letters, vol. 6, no. 5, pp. 303-313, 1987.
[34] P. Kovesi, "Image features from phase congruency," Videre: A Journal of Computer Vision Research, vol. 1, no. 3, pp. 1-26, 1999.
[35] N. B. Nill, "A Visual Model Weighted Cosine Transform for Image Compression and Quality Assessment," IEEE Transactions on Communications, vol. 33, no. 6, pp. 551-557, 1985.
[36] A. Ninassi, O. Le Meur, P. Le Callet and D. Barba, "Does where you Gaze on an Image Affect your Perception of Quality? Applying Visual Attention to Image Quality Metric," in IEEE International Conference on Image Processing, San Antonio, TX, 2007.
[37] D. M. Chandler and S. S. Hemami, "Suprathreshold Image Compression Based on Contrast Allocation and Global Precedence," in Proceedings of SPIE Human Vision and Electronic Imaging VIII, Santa Clara, CA, 2003.
[38] M. A. Georgeson and G. D. Sullivan, "Contrast constancy: deblurring in human vision by spatial frequency channels," The Journal of Physiology, vol. 252, no. 3, pp. 627-656, 1975.
[39] D. J. Simons and R. A. Rensink, "Change blindness: past, present, and future," Trends in Cognitive Sciences, vol. 9, no. 1, pp. 16-20, 2005.
[40] A. Mack and I. Rock, Inattentional Blindness, Bradford Rock, 1998.
[41] U. Rajashekar, I. van der Linde and A. C. Bovik, "GAFFE: A Gaze-Attentive Fixation Finding Engine," IEEE Transactions on Image Processing, vol. 17, no. 4, pp. 564-573, 2008.
[42] Y. Zhong, I. Richardson, S. Arash and P. McGeorge, "Influence of task and scene content on subjective video quality," in ICIAR (1)'04, Porto, Portugal, 2004.
[43] A. Eriko, N. Koyu, F. X. Takashi and N. Nagata, "Identification of Factors Related to the Enhancement of Image-Quality for Subjective Image-Quality Assessment Model Based on Psychological Measurement," in 4th International Conference on Human System Interactions (HSI), Yokohama, 2011.
[44] J. Lassalle, L. Gros and G. Coppin, "Combination of Physiological and Subjective Measures to Assess Quality of Experience for Audiovisual Technologies," in Third International Workshop on Quality of Multimedia Experience, Mechelen, Belgium, 2011.
[45] A. Mittal, A. K. Moorthy, W. Geisler and A. C. Bovik, "Task dependence of visual attention on compressed videos: point of gaze statistics and analysis," in Human Vision and Electronic Imaging XVI, San Francisco, CA, 2011.
[46] O. Le Meur, A. Ninassi, P. Le Callet and 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, vol. 25, no. 7, pp. 547-558, 2010.
[47] M. G. Albanesi and R. Amadeo, "A New Algorithm for Objective Video Quality Assessment on Eye Tracking Data," in 9th International Conference on Computer Vision Theory and Applications, Lisbon, 2014.
[48] M. G. Albanesi and R. Amadeo, "Impact of Fixation Time on Subjective Quality Metric: a New Proposal for Lossy Compression Impairment Assessment," World Academy of Science, Engineering and Technology, vol. 59, pp. 1604-1611, 2011.