  Full-reference image quality assessment (FR-IQA) techniques compare a
reference and a distorted/test image and predict the perceptual quality of the
test image in terms of a scalar value representing an objective score. The
evaluation of FR-IQA techniques is carried out by comparing the objective
scores from the techniques with the subjective scores (obtained from human
observers) provided in the image databases used for the IQA. Hence, we
reasonably assume that the goal of a human observer is to rate the distortion
present in the test image. The goal oriented tasks are processed by the human
visual system (HVS) through top-down processing which actively searches for
local distortions driven by the goal. Therefore local distortion measures in an
image are important for the top-down processing. At the same time, bottom-up
processing also takes place signifying spontaneous visual functions in the HVS.
To account for this, global perceptual features can be used. Therefore, we
hypothesize that the resulting objective score for an image can be derived from
the combination of local and global distortion measures calculated from the
reference and test images. We calculate the local distortion by measuring the
local correlation differences from the gradient and contrast information. For
global distortion, dissimilarity of the saliency maps computed from a bottom-up
model of saliency is used. The motivation behind the proposed approach has been
thoroughly discussed, accompanied by an intuitive analysis. Finally,
experiments are conducted in six benchmark databases suggesting the
effectiveness of the proposed approach that achieves competitive performance
with the state-of-the-art methods providing an improvement in the overall
performance.
