Fast Coding Unit Encoding Mechanism for Low Complexity Video Coding

Yuan Gao; Pengyu Liu; Yueying Wu; Kebin Jia; Guandong Gao

doi:10.1371/journal.pone.0151689

Fast Coding Unit Encoding Mechanism for Low Complexity Video Coding

PLoS One. 2016 Mar 21;11(3):e0151689. doi: 10.1371/journal.pone.0151689. eCollection 2016.

Authors

Yuan Gao^{1

2

3}, Pengyu Liu^{1

2

3}, Yueying Wu^{1

2

3}, Kebin Jia^{1

2

3}, Guandong Gao^{1

2

3}

Affiliations

¹ Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China.
² Beijing Laboratory of Advanced Information Networks, Beijing, China.
³ College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, China.

Abstract

In high efficiency video coding (HEVC), coding tree contributes to excellent compression performance. However, coding tree brings extremely high computational complexity. Innovative works for improving coding tree to further reduce encoding time are stated in this paper. A novel low complexity coding tree mechanism is proposed for HEVC fast coding unit (CU) encoding. Firstly, this paper makes an in-depth study of the relationship among CU distribution, quantization parameter (QP) and content change (CC). Secondly, a CU coding tree probability model is proposed for modeling and predicting CU distribution. Eventually, a CU coding tree probability update is proposed, aiming to address probabilistic model distortion problems caused by CC. Experimental results show that the proposed low complexity CU coding tree mechanism significantly reduces encoding time by 27% for lossy coding and 42% for visually lossless coding and lossless coding. The proposed low complexity CU coding tree mechanism devotes to improving coding performance under various application conditions.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Image Enhancement / methods*
Models, Theoretical
Probability
Video Recording / methods*

Grants and funding

This paper is supported by the Project for the Key Project of Beijing Municipal Education Commission under Grant No. KZ201310005004, Beijing Postdoctoral Research Foundation under Grant No.2015ZZ-23, China Postdoctoral Research Foundation under Grant No.2015M580029, and Computational Intelligence and Intelligent System of Beijing Key Laboratory Research Foundation under Grant No.002000546615004.