A psychovisual image Quality Metric based on multi-scale Structure Similarity

In this paper, a universal full-reference (FR) image quality metric based on Edge structure similarity (QMESS) is proposed using spatial position displacement degree of wavelet transform modulus maxima between reference image and distorted image in multi-resolution domain. Firstly, we decompose images in wavelet domain. The structure error between reference images and distorted images is computed based on the statistics of spatial position error of local modulus maxima in wavelet domain. At the same time, peak signal to noise ratio (PSNR) is adopted to evaluate the stochastic noise in images. Finally, the low frequency resolution layer distortion is evaluated by means of the mutual information and the luminance distortion. The three components are combined for the whole visual distortion measurement. From the experiment results, the proposed metric is much better than conventional PSNR method and the state-of-the-art SSIM approach in terms of the performance relative to subjective judgment. Comparing to the excellent VIF method, the proposed method performs better in individual distortions and obtains similar results on cross-distortion type.