Perceptual Reduced-Reference Visual Quality Assessment for Contrast Alteration

In image/video systems, contrast adjustment which manages to enhance visual quality is nowadays an important research topic. Yet very limited struggles have been made to the exploration of visual quality assessment for contrast adjustment. To tackle the issue, this paper proposes a novel reduced-reference (RR) quality metric with the integration of bottom-up and top-down strategies. The former one stems from the recently revealed free energy principle that tells that the human visual system seeks to comprehend an input image via uncertainty removal, while the latter one is toward using the symmetric Kullback-Leibler divergence to compare the histogram of the contrast-altered image with that of the pristine image. The bottom-up and top-down strategies are lastly incorporated to derive the RR contrast-altered image quality measure. A comparison using numerous existing IQA models is carried out on five contrast related databases/subsets in CID2013, CCID2014, CSIQ, TID2008, and TID2013, and experimental results validate the superiority of the proposed technique.