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} }