Video quality assessment based on motion structure partition similarity of spatiotemporal slice images

Peng Yan; Xuanqin Mou

doi:10.1117/1.JEI.27.3.033019

23 May 2018 Video quality assessment based on motion structure partition similarity of spatiotemporal slice images

Peng Yan, Xuanqin Mou

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 3, 033019 (May 2018). https://doi.org/10.1117/1.JEI.27.3.033019

Abstract

Video quality assessment (VQA) is becoming increasingly important as a comprehensive measure of video quality. This paper proposes a full-reference VQA (FR-VQA) algorithm based on the motion structure partition similarity of spatiotemporal slice (STS) images. To achieve this objective, a number of FR-image quality assessment algorithms were applied slice by slice to video STS images to compare their performance of detecting structure similarity of STS images. The algorithm that performed the best was selected to detect the similarity between motion-partitioning STS images. Next, as motion objects in the video sequence were found to have different influences on the prediction performance in terms of moving speed and track, the STS images were divided into simple and complex motion regions, and their contributions to the VQA task determined. Consequently, a promising effective and efficient VQA model, called STS-MSPS, is also proposed. Experimental evaluations conducted based on various annotated VQA databases indicate that the proposed STS-MSPS achieves state-of-the-art prediction performances in terms of correlations with subjective evaluation and statistical significance tests. This paper also shows that STS images by themselves provide sufficient information for VQA tasks and that the proposed complex motion region of an STS image is predominantly responsible for yielding a high-precision model.

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Peng Yan and Xuanqin Mou "Video quality assessment based on motion structure partition similarity of spatiotemporal slice images," Journal of Electronic Imaging 27(3), 033019 (23 May 2018). https://doi.org/10.1117/1.JEI.27.3.033019

Received: 25 August 2017; Accepted: 7 May 2018; Published: 23 May 2018

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