Structural similarity metrics for texture analysis and retrieval

Jana Zujovic*, Thrasyvoulos N Pappas, David L. Neuhoff

*Corresponding author for this work

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

51 Scopus citations

Abstract

The development of objective texture similarity metrics for image analysis applications differs fromthat of traditional image quality metrics because substantial point-by-point deviations are possible for textures that according to human judgment are essentially identical. Thus, structural similarity metrics (SSIM) attempt to incorporate "structural" information in image comparisons. The recently proposed structural texture similarity metric (STSIM) relies entirely on local image statistics. We extend this idea further by including a broader set of local image statistics, basing the selection on metric performance as compared to subjective evaluations. We utilize both intra- and inter-subband correlations, and also incorporate information about the color composition of the textures into the similarity metrics. The performance of the proposed metrics is compared to PSNR, SSIM, and STSIM on the basis of subjective evaluations using a carefully selected set of 50 texture pairs.

Original languageEnglish (US)
Title of host publication2009 IEEE International Conference on Image Processing, ICIP 2009 - Proceedings
PublisherIEEE Computer Society
Pages2225-2228
Number of pages4
ISBN (Print)9781424456543
DOIs
StatePublished - Jan 1 2009
Event2009 IEEE International Conference on Image Processing, ICIP 2009 - Cairo, Egypt
Duration: Nov 7 2009Nov 10 2009

Other

Other2009 IEEE International Conference on Image Processing, ICIP 2009
CountryEgypt
CityCairo
Period11/7/0911/10/09

Keywords

  • Dominant colors
  • Image compression
  • Image retrieval
  • Steerable filter decomposition

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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