Revisiting Perceptual Distortion for Natural Images: Mean Discrete Structural Similarity Index
Christopher J. Hillar, Sarah Marzen · 2017
A challenge in image processing is quantifying the perceptual quality of distorted images. Solutions to this problem allow lossy compression algorithms to be more easily and accurately evaluated. Motivated by failings of mean-squared error (MSE/PSNR), Wang, Bovik, and others proposed a perceptual image measure called mean structural similarity (MSSIM), which decomposes the distortion of image patches into three components: a difference in mean luminance, a difference in luminance variance, and a difference in structure. We present a new measure, mean discrete structural similarity (MDSSIM), that replaces the structural comparison of MSSIM with the Hamming distance between suitably discretized original and distorted image patches. To assess its performance, we apply this new image measure to a standard human psychophysics dataset, the LIVE Image Quality Assessment Database (Release 2). The high correlation of MDSSIM with human scores suggests, consistent with experiment and well-known results about lossy compression, that the human visual system may be fundamentally concerned with discrete structure in natural images.