Image quality assessment based on Prewitt magnitude

The goal of image quality assessment (IQA) research is to use computational models to calculate the quality of images consistently with subjective evaluations. In this paper, we propose a new image quality assessment (IQA) algorithm by combining Prewitt magnitude and regional mutual information (RMI) in HSV color space. The Prewitt operator is usually used for edge detection and can extract vertical edge more accurately than other operators. The HSV color space encapsulates information about a color in terms that are more natural and intuitive to humans. The proposed method PMRMI first transforms reference and distorted images from RGB color space into HSV color space and Prewitt magnitude is introduced to extract key edge features of each channel. Then the regional mutual information is calculated to measure the similarity of the two images. After that, a weighting method is utilized for better consistency with subjective evaluations. Therefore we get a single quality score. Experiments on various image distortion types demonstrate that the proposed algorithm can achieve better consistency with the subjective evaluations than PSNR and SSIM.