Reduced-Reference IQA in Contourlet Domain

Abstract

The human visual system (HVS) provides a suitable cue for image quality assessment (IQA). In this paper, we develop a novel reduced-reference (RR) IQA scheme by incorporating the merits from the contourlet transform, contrast sensitivity function (CSF), and Weber's law of just noticeable difference (JND). In this scheme, the contourlet transform is utilized to decompose images and then extract features to mimic the multichannel structure of HVS. CSF is applied to weight coefficients obtained by the contourlet transform to simulate the appearance of images to observers by taking into account many of the nonlinearities inherent in HVS. JND is finally introduced to produce a noticeable variation in sensory experience. Thorough empirical studies are carried out upon the Laboratory for Image and Video Engineering database against the subjective mean opinion score and demonstrate that the proposed framework has good consistency with subjective perception values and the objective assessment results can well reflect the visual quality of images.