Blind quality assessment of three-dimensional images using joint statistics of binocular rivalry and orientation-tuned responses

Jian Ma; Ping An; Liquan Shen; Kai Li

doi:10.1117/1.JEI.27.4.043016

18 July 2018 Blind quality assessment of three-dimensional images using joint statistics of binocular rivalry and orientation-tuned responses

Jian Ma, Ping An, Liquan Shen, Kai Li

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 4, 043016 (July 2018). https://doi.org/10.1117/1.JEI.27.4.043016

Abstract

Perceptual quality assessment of a three-dimensional (3-D) image is one of the most important tasks in various applications such as 3-D image coding, processing, enhancement, and monitoring system. But objective quality assessment of 3-D images is still a challenging task. Especially, blind quality assessment of 3-D images encounters an arduous challenge due to lack of prior information about the original images. To solve this problem, we propose a blind 3-D images’ quality evaluator by simulating binocular rivalry and orientation responses of the human visual system. As a main technical contribution of this research, both the low- and high-level binocular rivalry responses (BRR), as well as binocular orientation-tuned (BOT) responses, are considered for blind quality assessment of 3-D images. Specifically, the self-similarity of the BRR and BOT responses is extracted from the distorted 3-D images, which will change in the presence of distortions. Subsequently, all quality-aware features are mapped to subjective quality scores of the distorted 3-D images via using support vector regression. The performance of our algorithm is evaluated over two popular LIVE 3-D phase I and phase II databases and shown to be competitive with the state-of-the-art algorithms.

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Jian Ma, Ping An, Liquan Shen, and Kai Li "Blind quality assessment of three-dimensional images using joint statistics of binocular rivalry and orientation-tuned responses," Journal of Electronic Imaging 27(4), 043016 (18 July 2018). https://doi.org/10.1117/1.JEI.27.4.043016

Received: 15 January 2018; Accepted: 12 June 2018; Published: 18 July 2018

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