Video coding standards k. R. Rao, Do Nyeon Kim and Jae Jeong Hwang Springer 2014

REFERENCES ON SSIM

SSIM1. Z. Wang, et al., “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Processing, vol. 13, pp. 600–612, Apr. 2004

SSIM2. Z. Wang, L. Lu and A. C. Bovik, "Video quality assessment using structural distortion measurement", IEEE International Conference on Image Processing, Vol. 3, Page(s): 65 -68, Rochester, NY, September 22-25, 2002.

SSIM3. The SSIM Index for Image Quality Assessment http://www.ece.uwaterloo.ca/~z70wang/research/ssim/

SSIM4. What is 3-SSIM? 2/8/11 Alexander Parshin [[e-mail]]

It is our implementation of

http://live.ece.utexas.edu/publications/2010/li_jei_jan10.pdf

SSIM5. W. Malpica and A. Bovik, “Range image quality assessment by structural similarity”, IEEE ICASSP 2009, 19-24 April 2009.

SSIM6. J. Zujovic, T.N. Pappas and D.L. Neuhoff, “Structural similarity metrics for texture analysis and retrieval”, IEEE ICIP 2009, Cairo, Egypt, Nov. 2009 (paper MP.L1.5)

SSIM7. X. Shang, “Structural similarity based image quality assessment: pooling strategies and applications to image compression and digit recognition,” M.S. Thesis, EE Department, The University of Texas at Arlington, Aug. 2006.

SSIM8. Z. Wang and Q. Li, "Video quality assessment using a statistical model of human visual speed perception," Journal of the Optical Society of America A, vol. 24, no. 12, pp. B61-B69, Dec. 2007.

SSIM9. Z. Wang and X. Shang, “Spatial pooling strategies for perceptual image quality assessment,” IEEE International Conference on Image Processing, Atlanta, GA, Oct. 8-11, 2006.

SSIM10. Z. Wang and E. P. Simoncelli, “Translation insensitive image similarity in complex wavelet domain,” IEEE International Conference on Acoustics, Speech and Signal Processing, vol. II, pp. 573-576, Philadelphia, PA, Mar. 2005.

SSIM11. Z. Wang, L. Lu, and A. C. Bovik, “Video quality assessment based on structural distortion measurement,” Signal Processing: Image Communication, special issue on “Objective video quality metrics”, vol. 19, no. 2, pp. 121-132, Feb. 2004.

SSIM12. Z. Wang, E. P. Simoncelli and A. C. Bovik, “Multi-scale structural similarity for image quality assessment,” Invited Paper, IEEE 37^Th Asilomar Conference on Signals, Systems and Computers, vol.2, pp. 1398-1402, Nov. 2003.

Dr. Zhou Wang’s web site

http://www.ece.uwaterloo.ca/~z70wang/temp/DIP.zip

Anush K. Moorthy and Alan C. Bovik

Efficient motion weighted spatio-temporal video SSIM index

Proc. SPIE, Vol. 7527, 75271I (2010); doi:10.1117/12.844198 Conference Date: Monday 18 January 2010
Conference Location: San Jose, California, USA

Conference Title: Human Vision and Electronic Imaging XV

SSIM13. S. Wang, S. Ma and W. Gao, “SSIM based perceptual distortion rate optimization coding”, SPIE, VCIP, vol. 7744-91, Huangshan, China, July 2010.

Is & T Electronic Imaging, Science and Technology, SPIE, Image quality and system performance, vol. 7867, San Francisco, CA, Jan.2011. (plenary speech by A. Bovik).

SSIM14. Bhat, I. Richardson and S. Kanangara, “A new perceptual quality metric for compressed video based on mean squared error”, SP:IC, In press, 30 July, 2010.

SSIM15. Y.-T. Chen et al, “Evaluation of video quality by CWSSIM method”, SPIE, Mathematics of Data/Image Coding, Compression, and Encryption with Applications XII, VOL. 7799, pp. 7790T – 1 thru 7, Aug. 2010.

SSIM16. J. Wang et al, “Fractal image coding using SSIM”, IEEE ICIP 2011.

SSIM17. A. Rehman and Z. Wang, “SSIM based non-local means image denoising”, IEEE ICIP 2011.

SSIM18. W. Lin and C.-C. J. Kuo, “Perceptual visual quality metrics: A survey”, J. VCIR, vol.22, pp.297-312, May 2011.

SSIM19. C.Vu and S. Deshpande, “ViMSSIM: from image to video quality assessment”, ACMMoVid 12, Proc. 4^th workshop on mobile video, Chapel Hill, N.C., Feb. 2012.

SSIM20. A. Horne and D. Ziou, “Image quality metrics: PSNR vs. SSIM”, IEEE ICPR, pp.2366-2369, 2010.

SSIM21. C. Yeo, H.L. Tan and Y.H. Tan, “SSIM-based adaptive quantization in HEVC”, IEEE ICASSP, pp.1690-1694, Vancouver, Canada, 2013. (IVMSP-P3.2: Video coding II Poster).

“SSIM-based Error-resilient Rate Distortion Optimized H.264/AVC Video Coding for Wireless Streaming” Signal Processing: Image Communication (under review) June2013.

SSIM22. T.-S. Ou and Y.-H. Huang and H.H. Chen, “SSIM-based perceptual rate control for video coding”, IEEE Trans. CSVT, vol.21, pp.682-691, May 2011.

SSIM 23. A. Rehman and Z. Wang, “SSIM-inspired perceptual video coding for HEVC”, IEEE ICME 2012, pp.497-502, July 2012.

SSIM23. S. Wang et al, “SSIM-motivated rate distortion optimization for video coding”, IEEE Trans. CSVT, vol. 22, pp.516-529, April 2012.

SSIM24. C. Yeo, H.L. Tan and Y.H. Tan, “On rate distortion optimization using SSIM”, IEEE Trans. on CSVT, vol. 23, pp. 1170-1181, July 2013.

SSIM25. T. Zhou and Z. Wang, “On the use of SSIM in HEVC”, IEEE Asilomar conf. on circuits, systems and computers, pp. 1107 - 1111, Nov. 2013.

SSIM26. M. Hassan and C. Bhagvati, “Structural similarity measure for color images,” Int J Comput Appl, Vol. 43, pp. 7 – 12, 2012.

SSIM27. K. Naser, V. Ricordel and P. L. Callet, “Experimenting texture similarity metric STSIM for intra prediction mode selection and block partitioning in HEVC”, DSP, pp. 882 – 887, 2014.

SSIM28. S. Wang et al, “Perceptual Video Coding Based on SSIM – Inspired Divisive Normalization,” IEEE Trans. on Image Processing, vol.22, no.4, pp. 1418 – 1429, Apr. 2013.

SSIM29. A. Rehman and Z. Wang, “SSIM – Inspired Perceptual Video Coding for HEVC,” Proc. Int. Conf. Multimedia and Expo, pp. 497 – 502, Melbourne, Australia, July. 2012.

Emmy award for SSIM (Oct. 2015)

http://www.emmys.com/news/awards-news/sthash.c1DcwJf7.dpuf

Zhou Wang, Alan Bovik, Hamid Shiekh and Eero Simoncelli for Structural Similarity (SSIM) Video Quality Measurement Model. Structural Similarity (SSIM) is an algorithm for estimating the perceived quality of an image or video. Its computational simplicity and ability to accurately predict human assessment of visual quality has made it a standard tool in broadcast and post-production houses throughout the television industry.

SSIM uses powerful neuroscience-based models of the human visual system to achieve breakthrough quality prediction performance. Unlike previous complex error models that required special hardware, it can be easily applied in real time on common processor software. SSIM is now the most widely-used perceptual video quality measure, used to test and refine video quality throughout the global cable and satellite TV industry. It directly affects the viewing experiences of tens of millions of viewers daily.

S. Pateux and J. Jung, “An Excel add-in for computing Bjontegaard metric and its evolution,” VCEG Meeting, Marrakech, Morocco, Jan. 2007.

F. Bossen, “Excel template for BD-rate calculation based on Piece-wise Cubic Interpolation”, JCT-VC Reflector, 2011.

BD Metrics computation using MATLAB source code [Online] Available: http://www.mathworks.com/matlabcentral/fileexchange/41749-bjontegaard-metric-calculation--bd-psnr-/content/bjontegaard2.m BD Metrics: VCEG-M34. [Online] Available: http://wftp3.itu.int/av-arch/video-site/0104_Aus/VCEG-M34.xls