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The SSIM Index for Image Quality Assessment

The SSIM Index for Image Quality Assessment. Presented by: Wan Shu Cheng. Abstract. The Structural SIMilarity (SSIM) index is a novel method for measuring the similarity between two images.

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The SSIM Index for Image Quality Assessment

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  1. The SSIM Index for Image Quality Assessment Presented by: Wan Shu Cheng

  2. Abstract • The Structural SIMilarity (SSIM) index is a novel method for measuring the similarity between two images. • The SSIM index can be viewed as a quality measure of one of the images being compared, provided the other image is regarded as of perfect quality.

  3. PSNR error pooling

  4. Diagram of the Structural Similarity (SSIM) Measurement System

  5. Structural Similarity (SSIM) • Similarity measure • Luminance comparison • Contrast comparison • Structure comparison

  6. Specific form of SSIM Index Universal Quality Index (UQI)

  7. (a) Original (b) Salt-Pepper Noise • MSE=225 • SSIM=0.6494 (c)Additive Gaussian Noise • MSE=225 • SSIM=0.3891 (d)Multi-Speckle Noise • MSE=225 • SSIM=0.4408

  8. (a) Original (b) Contrast Stretching • MSE=225 • SSIM=0.9372 (c) Blurring • MSE=225 • SSIM=0.3461 (d) JPEG Compression • MSE=215 • SSIM=0.2876

  9. Source Code • Matlab Code • http://www.cns.nyu.edu/~zwang/files/research/ssim/ssim_index.m • C++ Code • http://mehdi.rabah.free.fr/SSIM/ • http://perso.orange.fr/reservoir/

  10. Reference • Image quality assessment: From error visibility to structural similarity, IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, Apr. 2004. • An adaptive linear system framework for image distortion analysis, to appear in IEEE International Conference on Image Processing, Genoa, Italy, Sept. 11-14, 2005. • 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. • 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. • Multi-scale structural similarity for image quality assessment, Invited Paper, IEEE Asilomar Conference on Signals, Systems and Computers, Nov. 2003. • Stimulus synthesis for efficient evaluation and refinement of perceptual image quality metrics, Human Vision and Electronic Imaging IX, Proc. SPIE, vol. 5292, Jan. 2004. • Structural Approaches to image quality assessment, to appear in Handbook of Image and Video Processing (Al Bovik, ed.), 2nd edition, Academic Press, June 2005. • A universal image quality index, IEEE Signal Processing Letters, vol. 9, no. 3, pp. 81-84, March 2002. • Why is image quality assessment so difficult? IEEE International Conference on Acoustics, Speech, & Signal Processing, May 2002. • Objective video quality assessment, in The Handbook of Video Databases: Design and Applications (B. Furht and O. Marqure, eds.), CRC Press, pp. 1041-1078, Sept. 2003.

  11. Thanks for Your Attention!

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