TY - JOUR
T1 - Machine Learning-Based Fast Angular Prediction Mode Decision Technique in Video Coding
AU - Ryu, Sookyung
AU - Kang, Jewon
N1 - Funding Information:
Manuscript received July 14, 2017; revised December 17, 2017, March 18, 2018, and May 3, 2018; accepted June 2, 2018. Date of publication July 18, 2018; date of current version August 14, 2018. This work was supported in part by the Institute for Information and Communications Technology Promotion grant funded by the Korea Government (MSIT) (No. 2018-0-00765, Development of Compression and Transmission Technologies for Ultra High Quality Immersive Videos Supporting 6DoF) and in part by the LG Electronics. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Yui-Lam Chan. (Corresponding author: Je-Won Kang.) The authors are with the Department of Electronic and Electrical Engineering, Ewha Womans University, Seoul 03760 , South Korea (e-mail: rsk19912@gmail.com; jewonk@ewha.ac.kr).
Publisher Copyright:
© 1992-2012 IEEE.
PY - 2018/11
Y1 - 2018/11
N2 - In this paper, we propose a machine learning-based fast intra-prediction mode decision algorithm, using random forest that is an ensemble model of randomized decision trees. The random forest is used to estimate an intra-prediction mode from a prediction unit and to reduce encoding time significantly by avoiding the intensive Rate-Distortion (R-D) optimization of a number of intra-prediction modes. To this aim, we develop a randomized tree model including parameterized split functions at nodes to learn directional block-based features. The feature uses only four pixels reflecting a directional property of a block, and, thus the evaluation is fast and efficient. To integrate the proposed technique into the conventional video coding standard frameworks, the intra-prediction mode derived from the proposed technique, called an inferred mode, is used to shrink the pool of the candidate modes before carrying out the R-D optimization. The proposed technique is implemented into the high efficiency video coding test model reference software of the state-of-the-art video coding standard and joint exploration model reference software, by integrating the random forest trained off-line into the codecs. Experimental results demonstrate that the proposed technique achieves significant encoding time reduction with only slight coding loss as compared with the reference software models.
AB - In this paper, we propose a machine learning-based fast intra-prediction mode decision algorithm, using random forest that is an ensemble model of randomized decision trees. The random forest is used to estimate an intra-prediction mode from a prediction unit and to reduce encoding time significantly by avoiding the intensive Rate-Distortion (R-D) optimization of a number of intra-prediction modes. To this aim, we develop a randomized tree model including parameterized split functions at nodes to learn directional block-based features. The feature uses only four pixels reflecting a directional property of a block, and, thus the evaluation is fast and efficient. To integrate the proposed technique into the conventional video coding standard frameworks, the intra-prediction mode derived from the proposed technique, called an inferred mode, is used to shrink the pool of the candidate modes before carrying out the R-D optimization. The proposed technique is implemented into the high efficiency video coding test model reference software of the state-of-the-art video coding standard and joint exploration model reference software, by integrating the random forest trained off-line into the codecs. Experimental results demonstrate that the proposed technique achieves significant encoding time reduction with only slight coding loss as compared with the reference software models.
KW - Fast intra prediction
KW - HEVC test model (HM)
KW - HEVC/H265
KW - fast mode decision
KW - joint exploration model (JEM)
KW - machine learning
KW - random forest
UR - http://www.scopus.com/inward/record.url?scp=85051809890&partnerID=8YFLogxK
U2 - 10.1109/TIP.2018.2857404
DO - 10.1109/TIP.2018.2857404
M3 - Article
AN - SCOPUS:85051809890
VL - 27
SP - 5525
EP - 5538
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
SN - 1057-7149
IS - 11
M1 - 8412615
ER -