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No-Reference JPEG image quality assessment based on Visual sensitivity

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Author(s): Zhang You-Sai | Chen Zhong-Jun

Journal: International Journal of Modern Education and Computer Science
ISSN 2075-0161

Volume: 3;
Issue: 1;
Start page: 45;
Date: 2011;
Original page

Keywords: Human visual sensitivity | support vector regression | neural network | image quality | No-reference assessment

ABSTRACT
in this paper, a novel human visual sensitivity measurement approach is presented to assessment the visual quality of JPEG-coded images without reference image. The key features of human visual sensitivity (HVS) such as edge amplitude and length, background activity and luminance are extracted from sample images as input vectors. SVR-NN was used to search and approximate the functional relationship between HVS and mean opinion score (MOS). Then, the measuring of visual quality of JPEG-coded images was realized. Experimental results prove that it is easy to initialize the network structure and set parameters of SVR-NN. And the better generalization performance owned by SVR-NN 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.
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