A nonparametric-test-based structural similarity measure for digital images

: In image processing, image similarity indices evaluate how much structural information is maintained by a processed image in relation to a reference image. Commonly used measures, such as mean squared error (MSE) and peak signal to noise ratio (PSNR), ignore the spatial information (e.g. redundancy) contained in natural images, which can lead to an inconsistent similarity evaluation from the human visual perception. Recently, a structural similarity measure (SSIM), that quantifies image fidelity through estimation of local correlations scaled by local brightness and contrast comparisons, was introduced by Wang et al. [2004]. This correlationbased SSIM outperforms MSE in the similarity assessment of natural images. However, as correlation only measures linear dependence, distortions from multiple sources or nonlinear image processing such as nonlinear filtering can cause SSIM to under or over estimate the true structural similarity. In this article, we propose a new similarity meas...

• Type: Journal
• Year: 2011
• Where: CSDA
• Authors: Haiyan Wang, Diego Maldonado, Sharad Silwal
• Keywords: CSDA 2011, Human Visual Perception, Image Similarity, Similarity Measure, Social Sciences