Saliency-induced reduced-reference quality index for natural scene and screen content images

Abstract

Massive content composed of both natural scene and screen content has been generated with the increasing use of wireless computing and cloud computing, which call for general image quality assessment (IQA) measures working for both natural scene images (NSIs) and screen content images (SCIs). In this paper, we develop a saliency-induced reduced-reference (SIRR) IQA measure for both NSIs and SCIs. Image quality and visual saliency are two widely studied and closely related research topics. Traditionally, visual saliency is often used as a weighting map in the final pooling stage of IQA. Instead, we detect visual saliency as a quality feature since different types and levels of degradation can strongly influence saliency detection. Image quality is described by the similarity between two images’ saliency maps. In SIRR, saliency is detected through a binary image descriptor called “image signature”, which significantly reduces the reference data. We perform extensive experiments on five large-scale NSI quality assessment databases including LIVE, TID2008, CSIQ, LIVEMD, CID2013, as well as two recently constructed SCI QA databases, i.e., SIQAD and QACS. Experimental results show that SIRR is comparable to state-of-the-art full-reference and reduced-reference IQA measures in NSIs, and it can outperform most competitors in SCIs. The most important is that SIRR is a cross-content-type measure, which works efficiently for both NSIs and SCIs. The MATLAB source code of SIRR will be publicly available with this paper.