Local binary pattern statistics feature for reduced reference image quality assessment

Min Zhang; Xuanqin Mou; Hiroshi Fujita; Lei Zhang; Xiangrong Zhou; Wufeng Xue

doi:10.1117/12.2008646

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4 February 2013 Local binary pattern statistics feature for reduced reference image quality assessment

Min Zhang, Xuanqin Mou, Hiroshi Fujita, Lei Zhang, Xiangrong Zhou, Wufeng Xue

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Proceedings Volume 8660, Digital Photography IX; 86600L (2013) https://doi.org/10.1117/12.2008646
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States

Abstract

Measurement of visual quality is of fundamental importance for numerous image and video processing applications. This paper presented a novel and concise reduced reference (RR) image quality assessment method. Statistics of local binary pattern (LBP) is introduced as a similarity measure to form a novel RR image quality assessment (IQA) method for the first time. With this method, first, the test image is decomposed with a multi-scale transform. Second, LBP encoding maps are extracted for each of subband images. Third, the histograms are extracted from the LBP encoding map to form the RR features. In this way, image structure primitive information for RR features extraction can be reduced greatly. Hence, new RR IQA method is formed with only at most 56 RR features. The experimental results on two large scale IQA databases show that the statistic of LBPs is fairly robust and reliable to RR IQA task. The proposed methods show strong correlations with subjective quality evaluations.

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Min Zhang, Xuanqin Mou, Hiroshi Fujita, Lei Zhang, Xiangrong Zhou, and Wufeng Xue "Local binary pattern statistics feature for reduced reference image quality assessment", Proc. SPIE 8660, Digital Photography IX, 86600L (4 February 2013); https://doi.org/10.1117/12.2008646

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