Internal generative mechanism inspired reduced reference image quality assessment with entropy of primitive

Abstract

In this paper, we propose a novel reduced-reference (RR) image quality assessment (IQA) algorithm based on the internal generative mechanism, which suggests that the human visual system (HVS) can actively predict the primary visual information and avoid the uncertainty. Specifically, the explanation of the visual scene is formulated as the process of sparse representation. In particular, the entropy of primitive accounts for the primary visual information and the discrepancy between the image signal and its best sparse description is regarded as the uncertainty in perception. As such, the combined feature that can summarize the primary visual information and uncertainty in sparse domain is required to be transmitted in the RR-IQA framework. Comparative studies of the proposed reduced reference metric is conduced on both single and multiple distortion databases, and experimental results demonstrate that the proposed metric can achieve high correlation with the human perception by only sending ignorable additional information.