Structured entropy of primitive: big data‐based stereoscopic image quality assessment

Abstract

The ultimate receiver of image and video is human visual system (HVS). It is an important problem in the domain of image and video processing that how to establish visual information representation model meeting the HVS perception property. In this study, authors give theory analysis and experiment results to prove that l_1 norm-based entropy of primitive (EoP) is superior to the l_0 norm-based EoP for the monocular cue in image quality assessment. By developing the concept of mutual information of primitive (MIP) as the binocular cue, an l_1 EoP-based stereoscopic image quality assessment metric is proposed. With EoP as monocular cue and MIP as binocular cue, the relative entropy between the original stereoscopic image and the distorted one is explored to predict the quality score with support vector regression. To avoid destroying image's structured information, the structured EoP (SEoP) is further explored to measure the stereoscopic image information. Extensive experimental results demonstrate that the stereoscopic image quality assessment algorithm with SEoP as monocular cue and MIP as binocular cue outperforms many state-of-the-art ones.