Structural texture similarity metrics for retrieval applications

Xiaonan Zhao*, Matthew G. Reyes, Thrasyvoulos N Pappas, David L. Neuhoff

*Corresponding author for this work

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

46 Scopus citations

Abstract

Traditional image similarity metrics compare two images on a point-by-point basis. On the other hand, structural similarity metrics (SSIM) attempt to base image similarity on "structural" information. We evaluate the performance of SSIM metrics in the context of texture similarity, and propose new metrics that incorporate the best features of SSIM and eliminate the most serious drawbacks. We show that the proposed new texture similarity metrics outperform SSIM and its variations, as well as PSNR and other traditional metrics. We demonstrate the advantages of the new metrics on a carefully selected set of 39 texture pairs and comparisons with informal subjective test results.

Original languageEnglish (US)
Title of host publication2008 IEEE International Conference on Image Processing, ICIP 2008 Proceedings
Pages1196-1199
Number of pages4
DOIs
StatePublished - Dec 1 2008
Event2008 IEEE International Conference on Image Processing, ICIP 2008 - San Diego, CA, United States
Duration: Oct 12 2008Oct 15 2008

Other

Other2008 IEEE International Conference on Image Processing, ICIP 2008
CountryUnited States
CitySan Diego, CA
Period10/12/0810/15/08

Keywords

  • Content-based retrieval
  • Texture similarity

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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