Reduced-reference image quality assessment based on perceptual image hashing

Quality monitoring is of great importance for online media broadcasting service. Without access to the original reference image in most practical scenarios, reduced-referenced (RR) image quality assessment is a good tradeoff and generally more reliable than no-reference (NR) metrics. In this paper, we propose employing image hashing features as side information to estimate the image quality. With its monotone sensitivity to the content quality degradation (e.g. due to compression), the proposed RR quality monitoring method based on our FJLT (Fast Johnson-Lindenstrauss transform) hashing provides two advantages: the accurate image quality estimate in term of conventional objective quality measure such as PSNR, and the low data rate required for delivering the partial reference information. Experimental results demonstrate that the proposed hashing-based RR quality measure system can accurately estimate the quality degradation due to JPEG and JPEG2000, the two widely adopted compression techniques in nowadays network transmission services.