No-reference image quality assessment based on structural and luminance information

Abstract

Research on no-reference image quality assessment (IQA) aims to develop a computational model simulating the human perception of image quality accurately and automatically without any prior information about the reference clean image signals. In this paper, we introduce a novel no-reference IQA metric, based on the analysis of structural degradation and luminance changes. Since the human visual system (HVS) is highly sensitive to structural distortion, we encode the image structural information as the local binary pattern (LBP) distribution. Besides, image quality is also affected by luminance changes, which cannot be captured properly by LBP threshold mechanism. Hence, the distribution of normalized luminance magnitudes is also included in the proposed IQA metric. Extensive experiments conducteics.