A HVS based Perceptual Quality Estimation Measure for Color Images

Human eyes are the best evaluation model for assessing the image quality as they are the ultimate receivers in numerous image processing applications. Mean squared error (MSE) and peak signal-to-noise ratio (PSNR) are the two most common full-reference measures for objective assessment of the image quality. These are well known for their computational simplicity and applicability for optimization purposes, but somehow fail to correlate with the Human Visual System (HVS) characteristics. In this paper a novel HVS based perceptual quality estimation measure for color images is proposed. The effect of error, structural distortion and edge distortion have been taken in account in order to determine the perceptual quality of the image contaminated with various types of distortions like noises, blurring, compression, contrast stretching and rotation. Subjective evaluation using Difference Mean Opinion Score (DMOS), is also performed for assessment of the perceived image quality. As depicted by the correlation values, the proposed quality estimation measure proves to be an efficient HVS based quality index. The comparisons in results also show better performance than conventional PSNR and Structural Similarity (SSIM).