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Game theory based no-reference perceptual quality assessment for stereoscopic images

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Abstract

In this paper, a no-reference perceptual quality assessment for stereoscopic image is proposed. Inspired by the binocular rivalry mechanism, the observation annoyance perception is explained as a bargain process. Game theory is exploited to model the rivalry of the left eye and right eye. The relation between annoyance perception with binocular disparity is further demonstrated and an annoyance map is calculated to simulate the observer perception. Then, with the consideration of the properties of HVS, the edge map and a saliency map are used to adjust the annoyance map. Finally, Minkowski pooling and multi-scale strategy are applied to compute the final score. We use the EPFL database to validate the proposed metric. The experimental results show that the final objective scores have a high degree of consistency with the subjective scores.

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References

  1. Tam W, Speranza F, Yano S (2011) Stereoscopic 3D-TV: visual comfort. IEEE Trans Broadcasting 57(2):335–346

    Article  Google Scholar 

  2. Lambooij M, Ijsselsteijn W, Fortuin M, Heynderickx I (2009) Visual discomfort and visual fatigue of stereoscopic displays: a review. J Imaging Sci Technol 53(3) 030201:1–14. 1, 2

  3. Howarth PA (2011) Potential hazards of viewing 3-D stereoscopic television, cinema and computer games: a review. Ophthalmic Physiol Opt 31(2):111–122. 1, 2

  4. Hoffman D, Girshick A, Akeley K, Banks M (2008) Vergence–accommodation conflicts hinder visual performance and cause visual fatigue. J Vis 8(3):1–30. 1, 2

  5. Zhu L, Zhao Y, Wang S, Chen H (2011) Spatial error concealment for stereoscopic video coding based on pixel matching. J Supercomput 58(1):96–105

    Article  Google Scholar 

  6. Bovik AC, Wang Z (2006) Modern image quality assessment. Morgan & Claypool, Morgan

    Google Scholar 

  7. Moorthy AK, Bovik AC (2011) Blind image quality assessment: from natural scene statistics to perceptual quality. IEEE Trans Image Process 20(12):3350–3364

    Article  MathSciNet  Google Scholar 

  8. Mittal A, Moorthy AK, Bovik AC (2012) Blind/referenceless image spatial quality evaluator. IEEE Trans Image Process 19(2):75–78

    MathSciNet  Google Scholar 

  9. Saad MA, Bovik AC (2012) Blind image quality assessment: a natural scene statistics approach in the DCT domain. IEEE Trans Image Process 21(8):3339–3352

    Article  MathSciNet  Google Scholar 

  10. Lu F, Wang H, Ji X, Er G (2009) Quality assessment of 3D asymmetric view coding using spatial frequency dominance model. Proc. 3DTV-CON, pp 1–4

  11. Jin L, Boev A, Gotchev A, Egiazarian K (2011) 3D-DCT based perceptual quality assessment of stereo video. Proc. ICIP, pp 2521–2524

  12. Benoit A, Le Callet P, Campisi P, Cousseau R (2008) Using disparity for quality assessment of stereoscopic images. 15th IEEE International Conference on Image Processing, pp 389–392

  13. Xing L, You J, Ebrahimi T, Perkis A (2010) Estimating quality of experience on stereoscopic images. 2010 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), pp 1–4

  14. Lambooij M, Ijsselsteijn W (2009) Visual discomfort and visual fatigue of stereoscopic displays: a review. J Imaging Sci Technol 53(3):3–12

    Article  Google Scholar 

  15. Goldmann L, De Simone F, Ebrahimi T (2010) Impact of acquisition distortions on the quality of stereoscopic images. 5th International Workshop on Video Processing and Quality Metrics for Consumer Electronics (VPQM)

  16. Chen M-J, Su C-C, Kwon D-K, Cormack LK, Bovik AC (2013) Full-reference quality assessment of stereopairs accounting for rivalry. Signal Process Image Commun 28(9):1143–1155

    Article  Google Scholar 

  17. Blake R, Westendorf DH, Overton R (1980) What is suppressed during binocular rivalry? Perception 9(2):223–231

    Article  Google Scholar 

  18. Kaplan IT, Metlay W (1964) Light intensity and binocular rivalry. J Exp Psychol 67(1):22–26

    Article  Google Scholar 

  19. Whittle P (1965) Binocular rivalry and the contrast at contours. Q J Exp Psychol 17(3):217–226

    Article  MathSciNet  Google Scholar 

  20. Levelt WJM (1968) On binocular rivalry. Mouton, The Hague

    Google Scholar 

  21. Fahle M (1982) Binocular rivalry: suppression depends on orientation and spatial frequency. Vis Res 22(7):787–800

    Article  Google Scholar 

  22. Logothetis NK, Schall JD (1989) Neuronal correlates of subjective visual perception. Science 245(4919):761–763

    Article  Google Scholar 

  23. Leopold DA, Logothetis NK (1996) Activity changes in early visual cortex reflect monkeys’ percepts during binocular rivalry. Nature 379(6565):549–553

    Article  Google Scholar 

  24. Alais D, Blake R (1999) Grouping visual features during binocular rivalry. Vis Res 39(26):4341–4353

    Article  Google Scholar 

  25. Blake R, Logothetis NK (2002) Visual competition. Nat Rev Neurosci 3(1):13–21

    Article  Google Scholar 

  26. Field DJ, Hayes A, Hess RF (1993) Contour integration by the human visual system: evidence for a local “association field”. Vis Res 33(2):173–193

    Article  Google Scholar 

  27. Kapadia MK, Ito M, Gilbert CD, Westheimer G (1995) Improvement in visual sensitivity by changes in local context: parallel studies in human observers and in V1 of alert monkeys. Neuron 15(4):843–856

    Article  Google Scholar 

  28. Nash JF Jr (1950) The bargaining problem. Econometrica 18(2):155–162

    Article  MathSciNet  MATH  Google Scholar 

  29. von Neumann J (1928) Zur theorie der gesellschaftsspiele. Math Annal 100(1):295–320

    Article  MATH  Google Scholar 

  30. Nash JF Jr (1951) Non-cooperative games. Annal Math 54(2):286–295

    Article  MathSciNet  MATH  Google Scholar 

  31. Osborne MJ, Rubistein A (1994) A course in game theory. MIT, Cambridge

    MATH  Google Scholar 

  32. Ahmad I, Luo J (2006) On using game theory to optimize the rate control in video coding. IEEE Trans Circuits Syst Video Technol 16(2):209–219

    Article  Google Scholar 

  33. Roughgarden T (1991) Stackelberg scheduling strategies. Proc ACM STOC 1991:104–113

    Google Scholar 

  34. Montet C, Serra D (2003) Game theory and economics, Houndmills, Basingstoke, Hampshire. Palgrave, New York

    Google Scholar 

  35. Sandholm T (1996) Limitations of Vickrey auction in computational multiagent systems. Proc. 2nd Int. Conf. Multiagent Syst, pp 299–306

  36. Alagoz BB (2008) Obtaining depth maps from color images by region based stereo matching algorithms. OncuBilim Algorithm Syst Labs 8(4):1–12

    Google Scholar 

  37. Wang Z, Bovik AC (2002) Why is image quality assessment so difficult. Proc IEEE Int Conf Acoust Speech Signal Process 4 IV: 3313–3316

  38. Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8(6):679–698

    Article  Google Scholar 

  39. Harel J, Koch C, Perona P (2007) Graph-based visual saliency. NIPS 545–552

  40. Teo PC, Heeger DJ (1994) Perceptual image distortion. Proc SPIE 2179:127–141

    Article  Google Scholar 

  41. De Simone F, Goldmann L, Baroncini V, Ebrahimi T (2009) Subjective evaluation of JPEG XR image compression, vol 7443. SPIE, San Diego

    Google Scholar 

  42. ITU-R (2000) Subjective assessment of stereoscopic television pictures. Tech Rep BT 1438

  43. Han J, Jiang T, Ma S (2012) Stereoscopic video quality assessment model based on spatial–temporal structural information. Proc. VCIP, pp 119–125

  44. Joveluro P, Malekmohamadi H, Fernando WAC, Kondoz AM (2010) Perceptual video quality metric for 3D video quality assessment. Proc. 3DTV-CON, pp 1–4

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Jiang, F., Bharanitharan, K., Barma, S. et al. Game theory based no-reference perceptual quality assessment for stereoscopic images. J Supercomput 71, 3337–3352 (2015). https://doi.org/10.1007/s11227-015-1412-1

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