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Reduced-Reference Image Quality Assessment Based on Discrete Cosine Transform Entropy
Yazhong ZHANG Jinjian WU Guangming SHI Xuemei XIE Yi NIU Chunxiao FAN
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2015/12/01
Online ISSN: 1745-1337
Type of Manuscript: PAPER
Category: Digital Signal Processing
reduced-reference, image quality assessment (IQA), human visual system (HVS), discrete cosine transform (DCT), entropy,
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Reduced-reference (RR) image quality assessment (IQA) algorithm aims to automatically evaluate the distorted image quality with partial reference data. The goal of RR IQA metric is to achieve higher quality prediction accuracy using less reference information. In this paper, we introduce a new RR IQA metric by quantifying the difference of discrete cosine transform (DCT) entropy features between the reference and distorted images. Neurophysiological evidences indicate that the human visual system presents different sensitivities to different frequency bands. Moreover, distortions on different bands result in individual quality degradations. Therefore, we suggest to calculate the information degradation on each band separately for quality assessment. The information degradations are firstly measured by the entropy difference of reorganized DCT coefficients. Then, the entropy differences on all bands are pooled to obtain the quality score. Experimental results on LIVE, CSIQ, TID2008, Toyama and IVC databases show that the proposed method performs highly consistent with human perception with limited reference data (8 values).