Structural similarity quality metrics in a coding context: Exploring the space of realistic distortions

Alan C. Brooks*, Thrasyvoulos N. Pappas

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations


Perceptual image quality metrics have explicitly accounted for human visual system (HVS) sensitivity to subband noise by estimating thresholds above which distortion is just-noticeable. A recently proposed class of quality metrics, known as structural similarity (SSIM), models perception implicitly by taking into account the fact that the HVS is adapted for extracting structural information (relative spatial covariance) from images. We compare specific SSIM implementations both in the image space and the wavelet domain. We also evaluate the effectiveness of the complex wavelet SSIM (CWSSIM), a translation-insensitive SSIM implementation, in the context of realistic distortions that arise from compression and error concealment in video transmission applications. In order to better explore the space of distortions, we propose models for typical distortions encountered in video compression/transmission applications. We also derive a multi-scale weighted variant of the complex wavelet SSIM (WCWSSIM), with weights based on the human contrast sensitivity function to handle local mean shift distortions.

Original languageEnglish (US)
Title of host publicationHuman Vision and Electronic Imaging XI - Proceedings of SPIE-IS and T Electronic Imaging
StatePublished - 2006
EventHuman Vision and Electronic Imaging XI - San Jose, CA, United States
Duration: Jan 16 2006Jan 18 2006

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


OtherHuman Vision and Electronic Imaging XI
CountryUnited States
CitySan Jose, CA


  • Error concealment
  • Human perception
  • Image quality
  • Structural similarity
  • Video coding
  • Video compression

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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