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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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Presentation Transcript
abstract
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.
structural similarity ssim
Structural Similarity (SSIM)
  • Similarity measure
  • Luminance comparison
  • Contrast comparison
  • Structure comparison
slide8
(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
slide9
(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
source code
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/
reference
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.