Opinion Aware Blind Image Quality Assessment - A Comparison of three Learning Approaches

Human society is living in the age of high speed development and increase of high volume of visual data. It has become a necessity to evaluate the quality of these visual data or images in many applications. This approach of quality assessment focuses on collecting feature vector from Natural Scene Images that reflects the shape parameter and correlation between adjacent pixels. A set of 36 features is collected from each image and training is done on images available in the data set. Then, three learning approaches – General Classification, KNN approach, and Distortion Specific approach are done on this feature vector. The 0training is done by taking the Mean Opinion Score (MOS) values available at the TID data set. Each method reflected its applicability and accuracy. It has been observed that distortion specific method out performs the other two. Specifically, the area where each one of these methods can be applied is also identified which is of great help to image manipulators. KeywordBlind/Non-Reference, Distortion Specific, Normalized Luminance, Quality Assessment

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