PROJECT TITLE :

Decision Fusion for Image Quality Assessment using an Optimization Approach

ABSTRACT:

The proliferation of electronic suggests that of communication entails distortion of visual info carried by processed pictures. Therefore, automatic analysis of image perceptual quality during a method that is in keeping with human perception is very important. During this letter, an approach to full-reference image quality assessment (IQA) is proposed. The perceptual quality of the image is evaluated using an aggregated decision of several IQA measures. An optimization downside of coming up with a decision fusion of 18 IQA measures is defined and solved employing a genetic algorithm. Obtained fusion strategies are evaluated on widely used massive image benchmarks and compared with thirty two state-of-the-art IQA approaches. Results of comparison reveal that the proposed approach outperforms other competing techniques.