Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 강제원 | * |
dc.date.accessioned | 2018-12-14T16:30:23Z | - |
dc.date.available | 2018-12-14T16:30:23Z | - |
dc.date.issued | 2018 | * |
dc.identifier.issn | 1057-7149 | * |
dc.identifier.issn | 1941-0042 | * |
dc.identifier.other | OAK-22894 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/247566 | - |
dc.description.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. | * |
dc.language | English | * |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | * |
dc.subject | Fast intra prediction | * |
dc.subject | fast mode decision | * |
dc.subject | machine learning | * |
dc.subject | random forest | * |
dc.subject | HEVC/H.265 | * |
dc.subject | HEVC test model (HM) | * |
dc.subject | joint exploration model (JEM) | * |
dc.title | Machine Learning-Based Fast Angular Prediction Mode Decision Technique in Video Coding | * |
dc.type | Article | * |
dc.relation.issue | 11 | * |
dc.relation.volume | 27 | * |
dc.relation.index | SCIE | * |
dc.relation.index | SCOPUS | * |
dc.relation.startpage | 5525 | * |
dc.relation.lastpage | 5538 | * |
dc.relation.journaltitle | IEEE TRANSACTIONS ON IMAGE PROCESSING | * |
dc.identifier.doi | 10.1109/TIP.2018.2857404 | * |
dc.identifier.wosid | WOS:000442340100013 | * |
dc.identifier.scopusid | 2-s2.0-85051809890 | * |
dc.author.google | Ryu, Sookyung | * |
dc.author.google | Kang, Je-Won | * |
dc.contributor.scopusid | 강제원(56367466400) | * |
dc.date.modifydate | 20240322125621 | * |