No-reference stereoscopic video quality assessment based on Tchebichef moment

Yuxin Chen; Mingyang Wen

doi:10.1117/12.2644706

12 October 2022 No-reference stereoscopic video quality assessment based on Tchebichef moment

Yuxin Chen, Mingyang Wen

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 1234227 (2022) https://doi.org/10.1117/12.2644706
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

Abstract

We propose a no-reference (NR) stereoscopic video quality assessment (SVQA) model based on Tchebichef moment in this paper. Specifically, we extract keyframes according to mutual information between adjacent frames, and then the extracted keyframes are segmented to patches to calculate low-order Tchebichef moments. Since the strong description ability of Tchebichef moment, and different order of Tchebichef moment can represent independent features with minimal information redundancy, we extract statistical features of Tchebichef moment on computed patches as spatial features. Considering the influence of distortions in spatiotemporal domain to video quality, we use the three-dimensional derivative of Gaussian filters to calculate the spatiotemporal energy responses and extract statistical features from the responses as spatiotemporal features. Finally, we combine the spatial and spatiotemporal features to predict the quality of stereoscopic videos. The proposed model is evaluated on the NAMA3DS1-COSPAD1, SVQA and Waterloo IVC phase I databases. The experimental results show that the proposed model achieved competitive performance as compared with existing SVQA models.

Citation Download Citation

Yuxin Chen and Mingyang Wen "No-reference stereoscopic video quality assessment based on Tchebichef moment", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 1234227 (12 October 2022); https://doi.org/10.1117/12.2644706

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KEYWORDS

Video

3D modeling

Databases

Performance modeling

Data modeling

Image quality

Feature extraction

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