  Image Quality Assessment (IQA) algorithms evaluate the perceptual quality of
an image using evaluation scores that assess the similarity or difference
between 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 intensity
adaptation and frequency scaling optimization at each filter-bank level and
spatial orientation to extract and enhance perceptually significant features.
Our computational model exploits the concept of object detection and
encapsulates characteristics proposed in other IQA algorithms in a unified
architecture. We also propose a systematic approach to review the evolution of
IQA algorithms using unbiased test datasets, instead of looking at individual
scores in isolation. Experimental results demonstrate the feasibility of our
approach.
