Content Adaptive Fast CU Size Selection for HEVC Intra-Prediction. In: 2019 IEEE International Conference on Consumer Electronics (ICCE), pp. 1–2, Institute of Electrical and Electronics Engineers (IEEE), 2019.
Abstract
This paper proposes a content adaptive fast CU size selection algorithm for HEVC intra-prediction using weighted support vector machines. The proposed algorithm demonstrates an average encoding time reduction of 52.38% with 1.19% average BDBR increase compared to HM16.1 reference encoder.
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@inproceedings{surrey851998,
title = {Content Adaptive Fast CU Size Selection for HEVC Intra-Prediction},
author = {Buddhiprabha Erabadda and Thanuja Mallikarachchi and Gosala Kulupana and Anil Fernando},
url = {http://epubs.surrey.ac.uk/851998/},
doi = {10.1109/ICCE.2019.8662119},
year = {2019},
date = {2019-03-01},
booktitle = {2019 IEEE International Conference on Consumer Electronics (ICCE)},
journal = {Proceedings of the 2019 IEEE International Conference on Consumer Electronics (ICCE)},
pages = {1--2},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
abstract = {This paper proposes a content adaptive fast CU size selection algorithm for HEVC intra-prediction using weighted support vector machines. The proposed algorithm demonstrates an average encoding time reduction of 52.38% with 1.19% average BDBR increase compared to HM16.1 reference encoder.},
keywords = {Adaptation models, Complexity theory, Encoding, Feature extraction, Inference algorithms, Signal processing algorithms, Support vector machines, University of Surrey},
pubstate = {published},
tppubtype = {inproceedings}
}