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3 months ago

Multiscale structural similarity for image quality assessment

{A.C. Bovik E.P. Simoncelli Z. Wang}

Multiscale structural similarity for image quality assessment

Abstract

The structural similarity image quality paradigm is based on the assumption that the human visual system is highly adapted for extracting structural information from the scene, and therefore a measure of structural similarity can provide a good approximation to perceived image quality. This paper proposes a multiscale structural similarity method, which supplies more flexibility than previous single-scale methods in incorporating the variations of viewing conditions. We develop an image synthesis method to calibrate the parameters that define the relative importance of different scales. Experimental comparisons demonstrate the effectiveness of the proposed method.

Benchmarks

BenchmarkMethodologyMetrics
video-quality-assessment-on-msu-sr-qa-datasetMS-SSIM
KLCC: 0.07821
PLCC: 0.16035
SROCC: 0.11017
Type: FR
video-quality-assessment-on-msu-sr-qa-datasetMS-SSIM Fast
KLCC: 0.18174
PLCC: 0.21800
SROCC: 0.24422
Type: FR
video-quality-assessment-on-msu-sr-qa-datasetMS-SSIM Superfast
KLCC: 0.16578
PLCC: 0.30014
SROCC: 0.21604
Type: FR
video-quality-assessment-on-msu-sr-qa-datasetMS-SSIM Precise
KLCC: 0.17468
PLCC: 0.20935
SROCC: 0.23108
Type: FR
video-quality-assessment-on-msu-video-quality-1MS-SSIM
KLCC: 0.7625
PLCC: 0.9375
SRCC: 0.9026

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Multiscale structural similarity for image quality assessment | Papers | HyperAI