Vol.39, No.2, 2021, pp.275-286, doi:10.32604/csse.2021.015255
OPEN ACCESS
ARTICLE
An Effective CU Depth Decision Method for HEVC Using Machine Learning
  • Xuan Sun1,2,3, Pengyu Liu1,2,3,*, Xiaowei Jia4, Kebin Jia1,2,3, Shanji Chen5, Yueying Wu1,2,3
1 Beijing University of Technology, Beijing, 100124, China
2 Beijing Laboratory of Advanced Information Networks, Beijing, 100124, China
3 Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China
4 Department of Computer Science, University of Pittsburgh, Pittsburgh, 15260, USA
5 School of Physics and Electronic Information Engineering, Qinghai Nationalities University, 810007, Qinghai, China
* Corresponding Author: Pengyu Liu. Email:
Received 12 November 2020; Accepted 14 December 2020; Issue published 20 July 2021
Abstract
This paper presents an effective machine learning-based depth selection algorithm for CTU (Coding Tree Unit) in HEVC (High Efficiency Video Coding). Existing machine learning methods are limited in their ability in handling the initial depth decision of CU (Coding Unit) and selecting the proper set of input features for the depth selection model. In this paper, we first propose a new classification approach for the initial division depth prediction. In particular, we study the correlation of the texture complexity, QPs (quantization parameters) and the depth decision of the CUs to forecast the original partition depth of the current CUs. Secondly, we further aim to determine the input features of the classifier by analysing the correlation between depth decision of the CUs, picture distortion and the bit-rate. Using the found relationships, we also study a decision method for the end partition depth of the current CUs using bit-rate and picture distortion as input. Finally, we formulate the depth division of the CUs as a binary classification problem and use the nearest neighbor classifier to conduct classification. Our proposed method can significantly improve the efficiency of inter-frame coding by circumventing the traversing cost of the division depth. It shows that the mentioned method can reduce the time spent by 34.56% compared to HM-16.9 while keeping the partition depth of the CUs correct.
Keywords
HEVC; inter-frame coding; CU depth
Cite This Article
Sun, X., Liu, P., Jia, X., Jia, K., Chen, S. et al. (2021). An Effective CU Depth Decision Method for HEVC Using Machine Learning. Computer Systems Science and Engineering, 39(2), 275–286.
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