PROJECT TITLE :

No-reference quality assessment for Multiplydistorted images in gradient domain - 2016

ABSTRACT:

In apply, pictures on the market to customers sometimes bear many stages of processing including acquisition, compression, transmission, and presentation, and each stage may introduce bound type of distortion. It's common that images are simultaneously distorted by multiple sorts of distortions. Most existing objective image quality assessment (IQA) strategies have been designed to estimate perceived quality of images corrupted by one image processing stage. During this letter, we propose a no-reference (NR) IQA technique to predict the visual quality of multiply-distorted images primarily based on structural degradation. Within the proposed methodology, a novel structural feature is extracted as the gradient-weighted histogram of local binary pattern (LBP) calculated on the gradient map (GWH-GLBP), which is effective to describe the advanced degradation pattern introduced by multiple distortions. In depth experiments conducted on two public multiply-distorted image databases have demonstrated that the proposed GWH-GLBP metric compares favorably with existing full-reference and NR IQA ways in terms of high accordance with human subjective ratings.