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Image Quality Assessment (IQA) algorithms evaluate the perceptual quality ofan image using evaluation scores that assess the similarity or differencebetween two images. We propose a new low-level feature based IQA technique,which applies filter-bank decomposition and center-surround methodology.Differing from existing methods, our model incorporates color intensityadaptation and frequency scaling optimization at each filter-bank level andspatial orientation to extract and enhance perceptually significant features.Our computational model exploits the concept of object detection andencapsulates characteristics proposed in other IQA algorithms in a unifiedarchitecture. We also propose a systematic approach to review the evolution ofIQA algorithms using unbiased test datasets, instead of looking at individualscores in isolation. Experimental results demonstrate the feasibility of ourapproach.

A Color Intensity Invariant Low Level Feature Optimization Framework for Image Quality Assessment