Structural similarity metrics for texture analysis and retrieval

The development of objective texture similarity metrics for image analysis applications differs from that 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 similari...

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Bibliographic Details
Published in2009 16th IEEE International Conference on Image Processing (ICIP) pp. 2225 - 2228
Main Authors Zujovic, J., Pappas, T.N., Neuhoff, D.L.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2009
Subjects
dominant colors
Humans
Image analysis
Image coding
Image color analysis
image compression
Image quality
Image retrieval
Image texture analysis
PSNR
Statistics
Steerable filter decomposition
Wavelet domain
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Summary:The development of objective texture similarity metrics for image analysis applications differs from that 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.
ISBN:9781424456536
1424456533
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2009.5413897