PROJECT TITLE :
Decision Fusion for Image Quality Assessment using an Optimization Approach
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.
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