Blind Stereoscopic Video Quality Evaluator
Abstract
Stereoscopic video quality assessment (SVQA) is a challenging problem. It has not been well investigated on how to measure depth perception quality independently under different distortion categories and degrees, especially exploit the depth perception to assist the overall quality assessment of 3D videos. In this paper, we propose a new depth perception quality metric (DPQM) and verify that it outperforms existing metrics on our published 3D video extension of High Efficiency Video Coding (3D-HEVC) video database. Furthermore, we validate its effectiveness by applying the crucial part of the DPQM to a novel blind stereoscopic video quality evaluator (BSVQE) for overall 3D video quality assessment. In the DPQM, we introduce the feature of auto-regressive prediction-based disparity entropy (ARDE) measurement and the feature of energy weighted video content measurement, which are inspired by the free-energy principle and the binocular vision mechanism. In the BSVQE, the binocular summation and difference operations are integrated together with the fusion natural scene statistic measurement and the ARDE measurement to reveal the key influence from texture and disparity. Experimental results on three stereoscopic video databases demonstrate that our method outperforms state-of-theart SVQA algorithms for both symmetrically and asymmetrically distorted stereoscopic video pairs of various distortion types.
Paper:
Zhibo Chen, Wei Zhou and Weiping Li, “Blind Stereoscopic Video Quality Assessment: From Depth Perception to Overall Experience,” IEEE Transactions on Image Processing, vol. 27, no. 2, pp. 721-734, 2018.
Get Source Code
We develop a depth perception quality metric according to the subjective experiment on the published 3D-HEVC Stereo Video Quality Database, and further extend to 3D overall quality assessment for the stereoscopic videos.
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