No-reference image and video quality estimation: Applications and human-motivated design

Signal Processing: Image Communication

Volumn 25, Issue 7, 2010, Pages 469-481

  Hemami, Sheila S ^a   Reibman, Amy R ^b  

^a Department of Obstetrics Gynecology   (United States)

^b AT AND T LABS RESEARCH   (United States)

Author keywords

Applications of quality metrics; Blind quality assessment; No reference; Quality estimator; Quality metrics; Video quality

Indexed keywords

BLIND QUALITY ASSESSMENTS; NO REFERENCES; QUALITY ESTIMATOR; QUALITY METRICS; VIDEO QUALITY;

ESTIMATION;

IMAGE QUALITY;

EID: 77955709595     PISSN: 09235965     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.image.2010.05.009     Document Type: Article

References (151)

1. M.P. Eckert, and A.P. Bradley Perceptual quality metrics applied to still image compression Signal Process. 70 1998 177 200
2. S. Winkler Issues in vision modeling for perceptual video quality assessment Signal Process. 78 2 1999 231 252
3. S. Winkler, and P. Mohandas The evolution of video quality measurement: from PSNR to hybrid metrics IEEE Trans. Broadcast. 54 3 2008 660 668
4. Z. Wang, and A.C. Bovik Mean squared error: love it or leave it? A new look at signal fidelity measures IEEE Signal Process. Mag. 26 1 2009 98 117
5. A.E. Savakis, S.P. Etz, and A.C. Loui Evaluation of image appeal in consumer photography SPIE vol. 3959 2000 111 120
6. R. Datta, J. Li, and J.Z. Wang Algorithmic inferencing of aesthetics and emotion in natural images: an exposition Proceedings of ICIP 2008 105 108
7. C. Li, and T. Chen Aesthetic visual quality assessment of paintings IEEE J. Sel. Top. Signal Process. 3 2 2009 236 252
8. E. Fedorovskaya, C. Neustaedter, and W. Hao Image harmony for consumer images Proceedings of ICIP 2008 121 124
9. R. Samadani, T. Mauer, D. Berfanger, J. Clark, and B. Bausk Representative image thumbnails: automatic and manual SPIE vol. 6806 2008
10. M.C.Q. Farias, and S.K. Mitra A methodology for designing no-reference video quality metrics VPQM 2009
11. M. Yuen, and H.R. Wu A survey of hybrid MC/DPCM/DCT video coding distortions Signal Process. 70 1998 247 278
12. A. Punchihewa, D.G. Bailey, Artefacts in image and video systems: classification and mitigation, in: Proceedings of Image and Vision Computing, New Zealand, 2002, pp. 197202.
13. ANSI T1.801.02-1996, Digital transport of video teleconferencing/video telephony signalsperformance terms, definitions, and examples, Technical Report, American National Standard for Telecommunication, 1996.
14. E. Vicario, I. Heynderickx, G. Ferretti, P. Carrai, Design of a tool to benchmark scaling algorithms on LCD monitors, in: SID Digest of Technical Papers, vol. 33, May 2002, pp. 704707.
15. A. Leontaris, P.C. Cosman, and A.R. Reibman Quality evaluation of motion-compensated edge artifacts in compressed video IEEE Trans. Image Process. 16 4 2007 943 956
16. E. Akyol, A.M. Tekalp, and M.R. Civanlar Content-aware scalability-type selection for rate adaptation of scalable video EURASIP J. Appl. Signal Process. 2007 1 2007 pp. 214214
17. I. Chong, H. Cheong, A. Ortega, New quality metric for multimedia compression using faulty hardware, in: International Workshop on Video Processing and Quality Metrics, 2006.
18. D. Nowroth, I. Polian, and B. Becker A study of cognitive resilience in a JPEG compressor IEEE International Conference on Dependable Systems and Networks June 2008 32 41
19. L. Chang, Y.-P. Tan, and H.-C. Chua Detection and removal of rainbow effects artifacts IEEE International Conference on Image Processing 2007 I-297 I-300
20. A.C. Kokaram On missing data treatment for degraded video and film archives: a survey and a new Bayesian approach IEEE Trans. Image Process. 13 3 2004 397 415
21. C.J. van den Branden Lambrecht Color moving pictures quality metric Proceedings of ICIP 1996 885 888
22. J. Lubin, M.H. Brill, A. De Vries, O. Finard, Method and apparatus for assessing the visibility of differences between two image sequences, US Patent 5,974,159, October 1999.
23. A.B. Watson Toward a perceptual video quality metric Human Vision and Electronic Imaging vol. 3299 1998 139 147
24. M. Masry, S.S. Hemami, and Y. Sermadevi A scalable wavelet-based video distortion metric and applications IEEE Trans. Circuits Syst. Video Technol. 16 2 2006 260 273
25. A.A. Michelson Studies in Optics 1927 University of Chicago Press
26. R.F. Hess, A. Bradley, and L. Piotrowski Contrast-coding in amblyopia. I. Differences in the neural basis of human amblyopia Proc. R. Soc. London Ser. B 217 1983 309 330
27. E. Peli Contrast in complex images J. Opt. Soc. Am. A 7 1990 2032 2040
28. B. Moulden, F.A.A. Kingdom, and L.F. Gatley The standard deviation of luminance as a metric for contrast in random-dot images Perception 19 1990 79 101
29. N. Graham Visual Pattern Analyzers 1989 Oxford University Press New York
30. G.E. Legge, and J.M. Foley Contrast masking in human vision J. Opt. Soc. Am. 70 1980 1458 1470
31. D.J. Heeger Normalization of cell responses in cat striate cortex Visual Neurosci. 9 1992 181 197
32. J.M. Foley Human luminance pattern mechanisms: masking experiments require a new model J. Opt. Soc. Am. A 11 1994 1710 1719
33. J.M. Foley, and C.C. Chen Pattern detection in the presence of maskers that differ in spatial phase and temporal offset: threshold measurements and a model Vision Res. 39 1999 3855 3872
34. A.B. Watson, and J.A. Solomon A model of visual contrast gain control and pattern masking J. Opt. Soc. Am. A 14 1997 2379 2391
35. A.B. Watson, M. Taylor, R. Borthwick, Image quality and entropy masking, in: Human Vision, Visual Processing, and Digital Display VIII, Proceedings of SPIE, vol. 3016, 1997, pp. 212.
36. S.S. Hemami, D.M. Chandler, B.G. Chern, J.A. Moses, Suprathreshold visual psychophysics and structure-based visual masking, in: Visual Communications and Image Processing, 2006.
37. J.G. Robson Spatial and temporal contrast-sensitivity functions of the visual system J. Opt. Soc. Am. 56 8 1966 1441 1442
38. A. Watanabe, T. Mori, S. Nagata, and K. Hiwatashi Spatial and temporal contrast-sensitivity functions of the visual system Vision Res. 8 9 1968 1245 1263
39. D.H. Kelly Motion and vision. II. Stabilized spatio-temporal threshold surface J. Opt. Soc. Am. 69 10 1979 1340 1349
40. R.F. Hess, and R.J. Snowden Temporal properties of human visual filters: number shapes and spatial covariation Vision Res. 32 1 1992 47 59
41. R.E. Fredericksen, and R.F. Hess Temporal detection in human vision: dependence on stimulus energy J. Opt. Soc. Am. A 14 10 1997 2557 2569
42. R.E. Fredericksen, and R.F. Hess Estimating multiple temporal mechanisms in human vision Vision Res. 38 7 1998 1023 1040
43. W.G. Owen Spatio-temporal integration in the human peripheral retina Vision Res. 12 1972 1011 1026
44. D. Kahneman Time intensity reciprocity under various conditions of adaptation and backward masking J. Exp. Psychol. 71 1966 543 549
45. E. Hering Zur lehre vom Lichtsinne 1878 Carl Gerolds and Sohn
46. D. Jameson, and L.M. Hurvich Some quantitative aspects of an opponent-colors theory. I. chromatic responses and spectral saturation J. Opt. Soc. Am. 45 7 1955 546 552
47. L.M. Hurvich, and D. Jameson An opponent-process theory of color vision Psychol. Rev. 64 10 1957 384 404
48. G.J.C. van der Horst, and M.A. Bouman Spatiotemporal chromaticity discrimination J. Opt. Soc. Am. 59 11 1969 1482 1488
49. E.M. Granger, and J.C. Heurtley Spatiotemporal chromaticity discrimination J. Opt. Soc. Am. 63 9 1973 1173 1174
50. D.H. Kelly Luminous and chromatic flickering patterns have opposite effects Science 188 4186 1975 371 372
51. R.L. DeValois, and K.K. DeValois Spatial Vision 1990 Oxford University Press
52. C.C. Koh, J.M. Foley, and S.K. Mitra Color preference and perceived color naturalness of digital videos SPIE vol. 6057 2006 257 267
53. C.C. Koh, J.M. Foley, and S.K. Mitra Color preference, color naturalness, and annoyance of compressed and color scaled digital videos SPIE vol. 6492 2007
54. B. Zhang, J.P. Allebach, and Z. Pizlo An investigation of perceived sharpness and sharpness metrics Proceedings of the SPIE vol. 5668 2005 98 110
55. B. Keelan Handbook of Image Quality: Characterization and Prediction 2002 Marcel Dekker, Inc
56. International Telecommunication Union, ITU-T recommendation P.930: principles of a reference impairment system for video, 1996.
57. M.C.Q. Farias, J.M. Foley, and S.K. Mitra Detectability and annoyance of synthetic blocky, blurry, noisy, and ringing artifacts IEEE Trans. Signal Process. 55 6 2007 2954 2964
58. R.R. Pastrana-Vidal, J.-C. Gicquel, C. Colomes, and H. Cherifi Sporadic frame dropping impact on quality perception SPIE vol. 5292 2004
59. R.R. Pastrana-Vidal, J.-C. Gicquel, C. Colomes, and H. Cherifi Temporal masking effect on dropped frames at video scene cuts SPIE vol. 5292 2004
60. Q. Huynh-Thu, and M. Ghanbari Temporal aspects of perceived quality in mobile video broadcasting IEEE Trans. Broadcast. 54 3 2008 641 651
61. M.S. Moore, J.M. Foley, and S.K. Mitra Defect visibility and content importance: effects on perceived impairment Signal Process. Image Commun. 19 2004 185 203
62. F. Boulos, B. Parrein, P. Le Callet, D.S. Hands, Perceptual effects of packet loss on H.264/AVC encoded videos, in: International Workshop on Video Processing and Quality Metrics, 2009.
63. S. Kanumuri, P.C. Cosman, A.R. Reibman, and V.A. Vaishampayan Modeling packet-loss visibility in MPEG-2 video IEEE Trans. Multimedia 8 2 2006 341 355
64. S. Kanumuri, S.G. Subramanian, P.C. Cosman, A.R. Reibman, Predicting H.264 packet loss visibility using a generalized linear model, in: IEEE International Conference on Image Processing, 811 October 2006, pp. 22452248.
65. G. Yadavalli, M. Masry, S.S. Hemami, Frame rate preferences in low bit rate video, in: IEEE International Conference on Image Processing, vol. 1, 1417 September 2003, pp. I-441I-444.
66. C.J. Hughes, M. Ghanbari, D.E. Pearson, V. Seferidis, and J. Xiong Modeling and subjective assessment of cell discard in atm video IEEE Trans. Image Process. 2 1993 212 222
67. S. Winkler, and R. Campos Video quality evaluation for Internet streaming applications SPIE 2003 104 115
68. J.B. Kruskal, and M. Wish Multidimensional Scaling 1986 Sage
69. J.-B. Martens Multidimensional modeling of image quality Proc. IEEE 90 1 2002 133 153
70. J.S. Goodman, and D.E. Pearson Multidimensional scaling of multiply-impaired television pictures IEEE Trans. Syst. Man Cybern. SMC-9 6 1979 353 356
71. J. Allnatt Transmitted Picture Assessment 1983 J. Wiley and Sons New York, NY
72. V. Kayargadde, and J.-B. Martens Perceptual characterization of images degraded by blur and noise: experiments J. Opt. Soc. Am. A 13 6 1996 1166 1177 (Pubitemid 126567672)
73. D.M. Chandler, K.H. Lim, and S.S. Hemami Effects of spatial correlations and global precedence on the visual fidelity of distorted images Human Vision and Electronic Imaging 2006
74. F. Kozamernik, P. Sunna, E. Wyckens, D.I. Pettersen, Subjective quality of internet video codecs phase II evaluations using SAMVIQ, EBU Technical Review, January 2005.
75. B. Keelan, and H. Urabe ISO 20462, a psychological image quality measurement standard SPIE vol. 5294 2004 181 189
76. Video Quality Experts Group (VQEG), Final report from the Video Quality Experts Group on the validation of objective models of video quality assessment, March 2000.
77. Video Quality Experts Group (VQEG), Final report from the video quality experts group on the validation of objective models of video quality assessment, phase II, August 2003.
78. Video Quality Experts Group (VQEG), Final report from the video quality experts group on the validation of objective models of multimedia quality assessment, phase I, September 2008.
79. CCIR, Recommendation 500-3: method for the subjective assessment of the quality of television pictures, Recommendations and Reports of the CCIR, International Telecommunication Union, Geneva, 1996.
80. ATIS-0800025, Test plan for evaluation of quality models for IPTV services, October 2009.
81. P. Corriveau Video quality testing H.R. Wu, K.R. Rao, Digital Video Image Quality and Perceptual Coding 2006 CRC Press 125 154 (Chapter 4)
82. C. Lee, H. Choi, E. Lee, S. Lee, and J. Choe Comparison of various subjective video quality assessment methods SPIE vol. 6059 2006
83. M. Knee, The picture appraisal rating (PAR)a single-ended picture quality measure for MPEG-2, in: Proceedings of International Broadcasting Convention, September 2000, pp. 95100.
84. D.S. Turaga, Y. Chen, and J. Caviedes No reference PSNR estimation for compressed pictures Signal Process. Image Commun. 19 2004 173 184
85. A. Ichigaya, M. Kurozumi, N. Hara, Y. Nishida, and E. Nakasu A method of estimating coding PSNR using quantized DCT coefficients IEEE Trans. Circuits Syst. Video Technol. 16 2 2006 251 259
86. A. Ichigaya, Y. Nishida, and E. Nakasu Nonreference method for estimating PSNR of MPEG-2 coded video by using DCT coefficients and picture energy IEEE Trans. Circuits Syst. Video Technol. 18 6 2008 817 826
87. T. Brando, M.P. Queluz, Blind PSNR estimation of video sequences using quantized DCT coefficient data, in: Picture Coding Symposium, 2007.
88. T. Brando, and M.P. Queluz No-reference image quality assessment based on DCT domain statistics Signal Process. 88 2008 822 833
89. A. Eden No-reference estimation of the coding PSNR for H.264-coded sequences IEEE Trans. Consum. Electron. 53 2 2007 667 674 (Pubitemid 47215085)
90. J. Choe, and C. Lee Estimation of the peak signal-to-noise ratio for compressed video based on generalized Gaussian modeling Opt. Eng. 46 10 2007
91. T. Brando, M.P. Queluz, No-reference PSNR estimation algorithm for H.264 encoded video sequences, in: EUSIPCO'08, 2008.
92. S.-Y. Shim, J.-H. Moon, and J.-K. Han PSNR estimation scheme using coefficient distribution of frequency domain in H.264 decoder Electron. Lett. 44 2 2008
93. A.R. Reibman, V.A. Vaishampayan, and Y. Sermadevi Quality monitoring of video over a packet network IEEE Trans. Multimedia 6 2 2004 327 334
94. A.R. Reibman, V. Vaishampayan, Low complexity quality monitoring of MPEG-2 video in a network, in: IEEE International Conference on Image Processing, vol. 3, 1417 September 2003, pp. III-261III-264.
95. M. Naccari, M. Tagliasacchi, F. Pereira, S. Tubaro, No-reference modeling of the channel induced distortion at the decoder for H.264/AVC video coding, in: IEEE ICIP, 2008.
96. K. Nishikawa, K. Munadi, and H. Kiya No-reference PSNR estimation for quality monitoring of motion JPEG2000 video over lossy packet networks IEEE Trans. Multimedia 10 4 2008 637 645
97. S.I. Olsen, Estimation of noise in images: an evaluation, in: CVGIP: Graphical Models Image Processing, vol. 55 (4), July 1993, pp. 319323.
98. X. Li, Blind image quality assessment, in: IEEE ICIP02, 2002.
99. A. Amer, and E. Dubois Fast and reliable structure-oriented video noise estimation IEEE Trans. Circuits Syst. Video Technol. 15 1 2005
100. M. Ghazal, A. Amer, and A. Ghrayeb A real-time technique for spatio-temporal video noise estimation IEEE Trans. Circuits Syst. Video Technol. 17 12 2007 1690 1699
101. V. Kayargadde, and J.-B. Martens An objective measure for perceived noise Signal Process. 49 3 1996 187 206
102. K. Rank, M. Lendl, and R. Unbehauen Estimation of image noise variance IEE Visual Image Signal Process. 164 1999 80 84
103. A.B. Watson, DCT quantization matrices optimized for individual images, in: SPIE Human Vision, Visual Processing, and Digital Display IV, 1993.
104. S. Winkler, A. Sharma, D. McNally, Perceptual video quality and blockiness metrics for multimedia streaming applications, in: International Symposium on Wireless Personal Multimedia Communications, 2001, pp. 547552.
105. H.R. Wu, and M. Yuen A generalized block-edge impairment metric for video coding IEEE Signal Process. Lett. 4 11 1997 317 320
106. S. Suthaharan A perceptually significant block-edge impairment metric for digital video coding Proceedings of International Conference on Acoustics, Speech, and Signal Processing 2003 III-681 III-684
107. R.V. Babu, S. Suresh, and A. Perkis No-reference JPEG-image quality assessment using GAP-RBF Signal Process. 87 2007 1493 1503
108. Z. Wang, A.C. Bovik, B.L. Evans, Blind measurement of blocking artifacts in images, in: IEEE International Conference on Image Processing, 2000.
109. S. Liu, and A.C. Bovik Efficient DCT-domain blind measurement and reduction of blocking artifacts IEEE Trans. Circuits Syst. Video Technol. 12 12 2002 1139 1149
110. G. Zhai, W. Zhang, X. Yang, W. Lin, and Y. Xu No-reference noticeable blockiness estimation in images Signal Process. Image Commun. 23 2008 417 432
111. L. Meesters, and J.-B. Martens A single-ended blockiness measure for JPEG-coded images Signal Process. 82 2002 369 387
112. R. Muijs, I. Kirenko, A no-reference block artifact measure for adaptive video processing, in: EUSIPCO '05, 2005.
113. R. Ferzli, and L.J. Karam A no-reference objective image sharpness metric based on the notion of just noticeable blur JNB IEEE Trans. Image Process. 18 4 2009 717 728
114. R.M. Rangayyan, and S.G. Elkadiki Algorithm for the computation of region-based image edge profile acutance J. Electron. Imag. 4 1 1995 62 70
115. P. Marziliano et al., Perceptual blur and ringing metrics: application to JPEG2000, Sig. Process. Image Commun., February 2004, pp. 163172.
116. N.G. Sadaka, L.J. Karam, R. Ferzli, G.P. Abousleman, A no-reference perceptual image sharpness metric based on saliency-weighted foveal pooling, in: IEEE International Conference on Image Processing, 1215 October 2008, pp. 369372.
117. E. Ong, W. Lin, Z. Lu, X. Yang, S. Yao, L. Jiang, F. Moschetti, A no-reference quality metric for measuring image blur, in: IEEE International Symposium on Signal Processing and its Applications, 2003, pp. 469472.
118. V. Kayargadde, J.-B. Martens, Estimation of edge parameters and imager blur using polynomial transforms, in: CVGIP: Graphical Models Image Process., vol. 56 (6), November 1994, pp. 442461.
119. J. Dijk, M. van Grinkel, R.J. van Asselt, L.J. van Vliet, P.W. Verbeek, A new sharpness measure based on Gaussian lines and edges, in: International Conference on Computer Analysis on Images and Patterns (CAIP), vol. 2756, 2003, pp. 149156.
120. X. Marichal, W.-Y. Ma, H.-J. Zhang, Blur determination in the compressed domain using DCT information, in: IEEE International Conference on Image Processing, 1999, pp. 386390.
121. K.-C. Yang, C.C. Guest, P.K. Das, Perceptual sharpness metric (PSM) for compressed video, in: IEEE International Conference on Multimedia and Expo, 2006.
122. G. Blanchet, L. Moisan, B. Roug, Measuring the global phase coherence of an image, in: IEEE International Conference on Image Processing, 2008, pp. 11761179.
123. J. Caviedes, and F. Oberti A new sharpness metric based on local kurtosis edge and energy information Signal Process. Image Commun. 19 10 2004 147 161
124. S.H. Oguz, Y.H. Hu, and T.Q. Nguyen Image coding ringing artifact reduction using morphologicalpost-filtering IEEE International Workshop on Multimedia Signal Processing 1998 628 633
125. X. Feng, and J.P. Allebach Measurement of ringing artifacts in JPEG images SPIE vol. 6076 2006
126. H. Liu, N. Klomp, I. Heynderickx, Perceptually relevant ringing region detection method, in: EUSIPCO '08, 2008.
127. H. Liu, N. Klomp, I. Heynderickx, A no-reference metric for perceived ringing, in: International Workshop on Video Processing and Quality Metrics, 2009.
128. A.G. Davis, D. Bayart, D.S. Hands, Hybrid no-reference video quality prediction, in: IEEE International Symposium on Broadband Multimedia Systems, 2009.
129. D. Hands, D. Bayart, A. Davis, A. Bourret, No reference perceptual quality metrics: approaches and limitations, in: Human Vision and Electronic Imaging XIV, 2009, p. 72400Y.
130. U. Engelke, H.-J. Zepernick, Pareto optimal weighting of structural impairments for wireless imaging quality assessment, in: IEEE International Conference on Image Processing, 2008, pp. 373376.
131. H.R. Sheikh, A.C. Bovik, and L. Cormak No-reference quality assessment using natural scene statistics: JPEG 2000 IEEE Trans. Image Process. 14 11 2005 1918 1927
132. R.W. Buccigrossi, and E.P. Simoncelli Image compression via joint statistical characterization in the wavelet domain IEEE Trans. Image Process. 8 12 1999 1688 1701
133. A.R. Reibman, S. Suthaharan, A no-reference spatial aliasing measure for digital image resizing, in: IEEE International Conference on Image Processing, 1215, October 2008, pp. 11841187.
134. S.E. Susstrunk, and S. Winkler Color image quality on the Internet SPIE vol. 5304 2004
135. T.J. Janssen, and F.J. Blommaert Predicting the usefulness and naturalness of color reproductions J. Imaging Sci. Technol. 44 2 2000 93 104
136. P.M.B. van Roosmalen, R.L. Lagendijk, and J. Biemond Correction of intensity flicker in old film sequences IEEE Trans. Circuits Syst. Video Technol. 9 7 1999 1013 1019
137. T. Vlachos Flicker correction for archived film sequences using a nonlinear model IEEE Trans. Circuits Syst. Video Technol. 14 4 2004 508 516
138. Y. Kuszpet, D. Kletsel, Y. Moshe, A. Levy, Post-processing for flicker reduction in H.264/AVC, in: Picture Coding Symposium, 2007.
139. M. Montenovo, A. Perot, M. Carli, P. Cicchetti, A. Neri, Objective quality evaluation of video services, in: International Workshop on Video Processing and Quality Metrics, 2006.
140. R.R. Pastrana-Vidal, J.-C. Gicquel, Automatic quality assessment of video fluidity impairments using a no-reference metric, in: International Workshop on Video Processing and Quality Metrics, 2006.
141. K. Watanabe, J. Okamoto, and T. Kurita Objective video quality assessment method for evaluating effects of freeze distortion in arbitrary video scenes SPIE Electronic Imaging vol. 6494 2007
142. K.-C. Yang, C.C. Guest, K. El-Maleh, and P.K. Das Perceptual temporal quality metric for compressed video IEEE Trans. Multimedia 9 7 2007 1528 1535
143. O. Verscheure, P. Frossard, and M. Hamdi User-oriented QoS analysis in MPEG-2 video delivery Real-Time Imaging 5 1999 305 314
144. S. Mohamed, and G. Rubino A study of real-time packet video quality using random neural networks IEEE Trans. Circuits Syst. Video Technol. 12 12 2002 1071 1083
145. R.V. Babu, A.S. Bopardikar, A. Perkis, O.I. Hillestad, No-reference metrics for video streaming applications, in: International Workshop on Packet Video, 2004.
146. H. Rui, C. Li, and S. Qiu Evaluation of packet loss impairment on streaming video Journal of Zhejiang University SCIENCE B 7 April 2006
147. D. Shabtay, N. Raviv, Y. Moshe, Video packet loss concealment detection based on image content, in: EUSIPCO '08, 2008.
148. T. Yamada, M. Yoshihiro, M. Serizawa, No-reference video quality estimation based on error-concealment effectiveness, in: Packet Video, 2007, pp. 288293.
149. R.A. Farrugia, and C.J. Debono A robust error detection mechanism for H.264/AVC coded video sequences based on support vector machines IEEE Trans. Circuits Syst. Video Technol. 18 12 2008 1766 1770
150. M.H. Brill, J. Lubin, P. Costa, S. Wolf, and J. Pearson Accuracy and cross-calibration of video-quality metrics: new methods from ATIS/T1A1 Signal Process. Image Commun. 19 2004 101 107
151. ATIS Technical Report T1.TR.77-2002, Data and sample program code to be used with the method specified in T1.TR.72-2001 for the calculation of resolving power of the video quality metrics in T1.TR.74-2001 and T1.TR.75-2001, January 2002.