Reduced-reference image quality assessment based on DCT Subband Similarity

Abstract

Reduced-reference image quality measures aim to estimate the visual quality of a distorted image with only partial information about the “perfect quality” reference image. In this paper, we present a reduced-reference image quality assessment (IQA) metric based on DCT Subbands Similarity (RR-DSS). According to the assumption that human visual perception is adapted for extracting structural information, the proposed technique measures change in structural information in subbands in the discrete cosine transform (DCT) domain and weights the quality estimates for these subbands. RR-DSS is simple to implement, incurs low computational complexity, and has a flexible tradeoff between the amount of side information and image quality estimation accuracy. RR-DSS was tested with public image databases and shows excellent correlation with human judgments of quality. It outperforms state-of-the-art RR IQA techniques and even several FR IQA techniques.