Share Email Print
cover

Proceedings Paper • new

A CU-level adaptive decision method for CNN-based in-loop filtering
Author(s): Yue Bei; Qi Wang; Zhipeng Cheng; Xinghao Pan; Jian Lei; Limin Wang; Dandan Ding
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Convolutional Neural Network (CNN) has been introduced to in-loop filtering in video coding for further performance improvement. For intra frame coding, a CNN model can be directly trained through learning the correlation between the reconstructed and the original frames, and the obtained model can then be applied to every single reconstructed frame to help improve the video quality. In contrast, for inter frame coding, intertwined reference dependency exists across frames. If a similar procedure of model training and deployment is adopted for inter as that for intra coding, over-smoothed reconstructed frames may be generated, which may further seriously deteriorate the overall coding performance. To address such an issue, state-of-the-art work resorts to the Rate Distortion Optimization (RDO) to determine whether to adopt the conventional or the CNN-based scheme for in-loop filtering, however leading to high computational complexity. In this paper, we propose a Coding-unit (CU) level Adaptive Decision approach (CAD) which employs an early decision for each CU, based on their coding parameters. Experimental results show that the proposed scheme achieves comparable performance with that of the RDO scheme while effectively reduces the encoding time complexity.

Paper Details

Date Published: 3 January 2020
PDF: 8 pages
Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 113731G (3 January 2020); doi: 10.1117/12.2557183
Show Author Affiliations
Yue Bei, MIGU Video Co., Ltd. (China)
Qi Wang, MIGU Video Co., Ltd. (China)
Zhipeng Cheng, MIGU Video Co., Ltd. (China)
Xinghao Pan, MIGU Video Co., Ltd. (China)
Jian Lei, Beijing Bravo Video Technologies, Inc. (China)
Limin Wang, Beijing Bravo Video Technologies, Inc. (China)
Dandan Ding, Beijing Bravo Video Technologies, Inc. (China)


Published in SPIE Proceedings Vol. 11373:
Eleventh International Conference on Graphics and Image Processing (ICGIP 2019)
Zhigeng Pan; Xun Wang, Editor(s)

© SPIE. Terms of Use
Back to Top