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Multiscale structural similarity for image quality assessment
{A.C. Bovik E.P. Simoncelli Z. Wang}

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