7 March 2016 Stereoscopic image quality assessment using disparity-compensated view filtering
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Abstract
Stereoscopic image quality assessment (IQA) plays a vital role in stereoscopic image/video processing systems. We propose a new quality assessment for stereoscopic image that uses disparity-compensated view filtering (DCVF). First, because a stereoscopic image is composed of different frequency components, DCVF is designed to decompose it into high-pass and low-pass components. Then, the qualities of different frequency components are acquired according to their phase congruency and coefficient distribution characteristics. Finally, support vector regression is utilized to establish a mapping model between the component qualities and subjective qualities, and stereoscopic image quality is calculated using this mapping model. Experiments on the LIVE 3-D IQA database and NBU 3-D IQA databases demonstrate that the proposed method can evaluate stereoscopic image quality accurately. Compared with several state-of-the-art quality assessment methods, the proposed method is more consistent with human perception.
© 2016 SPIE and IS&T
Yang Song, Yang Song, Mei Yu, Mei Yu, Gangyi Jiang, Gangyi Jiang, Feng Shao, Feng Shao, Zongju Peng, Zongju Peng, } "Stereoscopic image quality assessment using disparity-compensated view filtering," Journal of Electronic Imaging 25(2), 023001 (7 March 2016). https://doi.org/10.1117/1.JEI.25.2.023001 . Submission:
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