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학술지 Video Quality Model of Compression, Resolution and Frame Rate Adaptation Based on Space-Time Regularities
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저자
이대열, 김종호, 고현석, Alan C. Bovik
발행일
202205
출처
IEEE Transactions on Image Processing, v.31, pp.3644-3656
ISSN
1057-7149
출판사
IEEE
DOI
https://dx.doi.org/10.1109/TIP.2022.3173810
협약과제
22HH6100, [통합과제] 초실감 테라미디어를 위한 AV부호화 및 LF미디어 원천기술 개발, 최진수
초록
Being able to accurately predict the visual quality of videos subjected to various combinations of dimension reduction protocols is of high interest to the streaming video industry, given rapid increases in frame resolutions and frame rates. In this direction, we have developed a video quality predictor that is sensitive to spatial, temporal, or space-time subsampling combined with compression. Our predictor is based on new models of space-time natural video statistics (NVS). Specifically, we model the statistics of divisively normalized difference between neighboring frames that are relatively displaced. In an extensive empirical study, we found that those paths of space-time displaced frame differences that provide maximal regularity against our NVS model generally align best with motion trajectories. Motivated by this, we built a new video quality prediction engine that extracts NVS features that represent how space-time directional regularities are disturbed by space-time distortions. Based on parametric models of these regularities, we compute features that are used to train a regressor that can accurately predict perceptual quality. As a stringent test of the new model, we apply it to the difficult problem of predicting the quality of videos subjected not only to compression, but also to downsampling in space and/or time. We show that the new quality model achieves state-of-the-art (SOTA) prediction performance on the new ETRI-LIVE Space-Time Subsampled Video Quality (STSVQ) and also on the AVT-VQDB-UHD-1 database.
KSP 제안 키워드
Dimension Reduction, Empirical study, Frame rate, Maximal regularity, Motion trajectory, New model, Parametric models, Perceptual Quality, Prediction engine, Quality model, Rate adaptation