Reference frame list optimization algorithm in video coding by quality enhancement of the nearest picture

被引：0
|
作者：
Huo J. [1 ]
Qiu R. [1 ]
Ma Y. [1 ]
Yang F. [1 ]
机构：
[1] State Key Laboratory of Integrated Services Network, Xidian University, Xi’an
来源：
Tongxin Xuebao/Journal on Communications | 2022年 / 43卷 / 11期
基金：
中国国家自然科学基金;
关键词：
deep learning; H.265/HEVC; interframe prediction; reference frame list;
D O I：
10.11959/j.issn.1000-436x.2022185
中图分类号：
学科分类号：
摘要：
Interframe prediction is a key module in video coding, which uses the samples in the reference frames to predict those in the current picture, thus helps to represent the complex video by transmitting a small amount of the prediction residual. In lossy video coding, the qualities of reference frames are affected by the quantization distortion, which lead to poor prediction accuracy and performance degradation. Targeted at the low latency video services, a reference frame list optimization algorithm was proposed, which enhanced the quality of the nearest reference frame by a deep learning-based convolutional neural network, and integrated the enhanced reference frame into the reference frame list to improve the accuracy of interframe prediction. Compared with H.265/HEVC reference software HM16.22, the proposed algorithm provides BD-rate savings of 9.06%, 14.92% and 13.19% for Y, Cb and Cr components, respectively. © 2022 Editorial Board of Journal on Communications. All rights reserved.
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页码：136 / 147
页数：11
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