A brain-inspired computational model for extremely few reference image quality assessment

Abstract

In the study of brain science, the free energy principle and attention perception mechanism have been the two of the most critical findings during the past few decades, arousing a wide range of attention and valuable applications from the research fields of image and video processing, computer vision, etc. Motivated by the aforementioned two important findings, we in this paper develop a brain-inspired computational model for extremely few reference image quality assessment (IQA), dubbed as BCM. The proposed BCM implements with the two main steps. First, we combine free energy principle and sparse perception mechanism to achieve the goal of only using extremely few reference for assessing the image quality. Second, we further introduce the attention perception mechanism to boost the assessment performance by improving the sparse perception mechanism mentioned above. Based on the most commonly used image quality database, it was found that our proposed model has derived higher performance than the peer extremely few reference IQA models and competitive performance as compared with the benchmark full reference IQA models.