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Machine Learning-Based Fast Angular Prediction Mode Decision Technique in Video Coding

Title
Machine Learning-Based Fast Angular Prediction Mode Decision Technique in Video Coding
Authors
Ryu S.Kang J.
Ewha Authors
강제원
SCOPUS Author ID
강제원scopus
Issue Date
2018
Journal Title
IEEE Transactions on Image Processing
ISSN
1057-7149JCR Link
Citation
vol. 27, no. 11, pp. 5525 - 5538
Keywords
Fast intra predictionfast mode decisionHEVC test model (HM)HEVC/H265joint exploration model (JEM)machine learningrandom forest
Publisher
Institute of Electrical and Electronics Engineers Inc.
Indexed
SCI; SCIE; SCOPUS WOS scopus
Abstract
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. © 1992-2012 IEEE.
DOI
10.1109/TIP.2018.2857404
Appears in Collections:
엘텍공과대학 > 전자공학과 > Journal papers
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