Research On NO-reference Image Quality Assessment Based On HVS

no-reference image quality evaluation method had gained a universal attation. Because the essence of image serving people determined that its assessment result must accord with human visual system character, the purpose of image quality assessment is derived to design an effective algorithm which is highly consistent with human sbjective evaluation value of visual perception.The most popular and widely used image format in the Internet as well as in digital cameras happens to be JPEG. Therefore, research object of this paper is NO-reference JPEG image quality assessment based human visual system. The main research results of this paper can be summarized as follows:1. Summarizing the image quality assessment methods. The no-reference image quality measurement is stressed and then several important no-reference methods are introduced.2. In this paper, texture edge masking and luminance masking characters are respectively extracted using several of mathematical models and then integrated into a masking map. Human visual sensitivity features such as edge amplitude, edge length, background activity and background luminance are extracted through several filtering operators. The experimental results show that the extracted features all have better discrimination.3.The No-Reference image quality assessment metric based on masking is presented to predict JPEG image qulity. The masking values on the 8×8 block boundaries are extracted using DCT block discontinuity detection and then can be easily pooled with a Minkowski summation to generate the Mean noticeable Blockiness Score to evaluate image quality. The compared experimental results demonstrate that the metric is highly consistent with mean subjective score, well evaluating image quality.4. The No-Reference image quality assessment metric based on human visual sensitivity is presented. The support vector regression naural network algorithm is used to search and approximate the functional relationship between human visual sensitivity features and mean subjective score. Then, the measuring of visual quality of JPEG-coded images was realized considering human visual sensitivity features such as edge amplitude, edge length, background activity and background luminance. Experimental results prove that its better generalization performance can add the new features of the sample automatically. Compared with other image quality metrics, the experimental results of the proposed metric exhibit much higher correlation with perception character of HVS. And the role of HVS feature in image quality index is fully reflected.