Visual structural degradation based reduced-reference image quality assessment

Abstract

Reduced-reference (RR) image quality assessment (IQA) aims to use less reference data for quality evaluation. Global features are demanded to effectively express quality degradation caused by distortion. Since the human visual system (HVS) is highly sensitive to structure degradation, we suggest to represent the visual content of an image with several structural patterns. Also, image quality is measured based on the structural degradation on these patterns. Firstly, the spatial correlation of image structure is analyzed with the local binary pattern (LBP), and several representative structural patterns are extracted. Then, the energy changes on these patterns between the reference image and the distorted image are calculated. Finally, the support vector regression (SVR) procedure is adopted for feature pooling, and the quality score of the input image is returned. Experimental results for three publicly available databases demonstrate that the proposed RR IQA method is highly consistent with the human perception, which uses a small amount of reference data (20 values) and achieves a promising improvement on quality evaluation accuracy.