  Objective image quality assessment (IQA) is imperative in the current
multimedia-intensive world, in order to assess the visual quality of an image
at close to a human level of ability. Many~parameters such as color intensity,
structure, sharpness, contrast, presence of an object, etc., draw human
attention to an image. Psychological vision research suggests that human vision
is biased to the center area of an image and display screen. As a result, if
the center part contains any visually salient information, it draws human
attention even more and any distortion in that part will be better perceived
than other parts. To the best of our knowledge, previous IQA methods have not
considered this fact. In this paper, we propose a full reference image quality
assessment (FR-IQA) approach using visual saliency and contrast; however, we
give extra attention to the center by increasing the sensitivity of the
similarity maps in that region. We evaluated our method on three large-scale
popular benchmark databases used by most of the current IQA researchers
(TID2008, CSIQ~and LIVE), having a total of 3345 distorted images with
28~different kinds of distortions. Our~method is compared with 13
state-of-the-art approaches. This comparison reveals the stronger correlation
of our method with human-evaluated values. The prediction-of-quality score is
consistent for distortion specific as well as distortion independent cases.
Moreover, faster processing makes it applicable to any real-time application.
The MATLAB code is publicly available to test the algorithm and can be found
online at http://layek.khu.ac.kr/CEQI.
