A No Reference Image Quality Assessment Metric Based on Visual Perception

• Main Authors: Yan Fu, Shengchun Wang
• Format: Article
• Language: English
• Published: MDPI AG 2016-12-01
• Series: Algorithms
• Subjects: image quality assessment; no reference; visual perception; support vector regression; region of interest; generalized Gauss distribution (GGD)
• Online Access: http://www.mdpi.com/1999-4893/9/4/87

Nowadays, how to evaluate image quality reasonably is a basic and challenging problem. In view of the present no reference evaluation methods, they cannot reflect the human visual perception of image quality accurately. In this paper, we propose an efficient general-purpose no reference image quality assessment (NRIQA) method based on visual perception, and effectively integrates human visual characteristics into the NRIQA fields. First, a novel algorithm for salient region extraction is presented. Two characteristics graphs of texture and edging of the original image are added to the Itti model. Due to the normalized luminance coefficients of natural images obey the generalized Gauss probability distribution, we utilize this characteristic to extract statistical features in the regions of interest (ROI) and regions of non-interest respectively. Then, the extracted features are fused to be an input to establish the support vector regression (SVR) model. Finally, the IQA model obtained by training is used to predict the quality of the image. Experimental results show that this method has good predictive ability, and the evaluation effect is better than existing classical algorithms. Moreover, the predicted results are more consistent with human subjective perception, which can accurately reflect the human visual perception to image quality.