Gradient-Based Structural Similarity for Image Quality Assessment

By Guan-Hao Chen, Chun-Ling Yang, Sheng-Li Xie. International Conference on Information Photonics, 2006

301 citations recorded by the source corpus.

How researchers see this paper

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PieAPP: Perceptual Image-Error Assessment Through Pairwise Preference (2018, methodology): “…visual system (HVS) such as contrast sensitivity [22], high-level structural acuity [52], and masking [48, 51], or use other statistics/features [3,4,14,15,38,44–46, 53, To address these limitations, some have proposed IQA methods based on machine learning to learn more sophisticated models [19].”
Image Quality Assessment Based on Gradient Similarity (2012, unknown): “For example, the structure comparison component has been changed [19], [45] to the gradient domain, or both the contrast and structure comparison components have been modified [20].”
Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index (2013, background): “The traditional mean square error (MSE) or peak signal to noise ratio (PSNR) correlates poorly with human perception, and hence researchers have been devoting much effort in developing advanced perception-driven image quality assessment (IQA) models [2, 25].”
Brain Imaging Generation with Latent Diffusion Models (2022, methodology): “We also measured the generation diversity with the Multi-Scale Structural Similarity Metric (MS-SSIM) and 4-G-R-SSIM [24,4,19], where a value close to 0 suggests high diversity.”