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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, Sookyung; Kang, Je-Won
- Ewha Authors
- 강제원
- SCOPUS Author ID
- 강제원
- Issue Date
- 2018
- Journal Title
- IEEE TRANSACTIONS ON IMAGE PROCESSING
- ISSN
- 1057-7149
1941-0042
- Citation
- IEEE TRANSACTIONS ON IMAGE PROCESSING vol. 27, no. 11, pp. 5525 - 5538
- Keywords
- Fast intra prediction; fast mode decision; machine learning; random forest; HEVC/H.265; HEVC test model (HM); joint exploration model (JEM)
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Indexed
- SCIE; SCOPUS
- Document Type
- Article
- 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.
- DOI
- 10.1109/TIP.2018.2857404
- Appears in Collections:
- 공과대학 > 전자전기공학전공 > Journal papers
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