TY - GEN
T1 - Fast intra prediction mode decision for HEVC using random forest
AU - Yan, Zhuge
AU - Cho, Siu Yeung
AU - Welsen, Sherif
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery. All rights reserved.
PY - 2019
Y1 - 2019
N2 - In this paper, we extracted specific image features that represent CU texture, incorporate a machine learning technique, namely random forest, in HEVC intra prediction mode selection, to improve the performance of intra coding of HEVC. Compared with similar algorithms, our method extracts very specific features of image texture changes in terms of angle. Therefore the proposed method can achieve very high prediction accuracy. Having similar reduction in complexity, the proposed algorithm can gain higher video quality compared with similar algorithms. 2019 Copyright is held by the owner/author(s).
AB - In this paper, we extracted specific image features that represent CU texture, incorporate a machine learning technique, namely random forest, in HEVC intra prediction mode selection, to improve the performance of intra coding of HEVC. Compared with similar algorithms, our method extracts very specific features of image texture changes in terms of angle. Therefore the proposed method can achieve very high prediction accuracy. Having similar reduction in complexity, the proposed algorithm can gain higher video quality compared with similar algorithms. 2019 Copyright is held by the owner/author(s).
KW - HEVC
KW - Intra prediction
KW - Random forest
UR - http://www.scopus.com/inward/record.url?scp=85065790450&partnerID=8YFLogxK
U2 - 10.1145/3317640.3317645
DO - 10.1145/3317640.3317645
M3 - Conference contribution
AN - SCOPUS:85065790450
SN - 9781450361750
T3 - ACM International Conference Proceeding Series
SP - 45
EP - 49
BT - ACM International Conference Proceeding Series
PB - Association for Computing Machinery
T2 - 2019 International Conference on Image, Video and Signal Processing, IVSP 2019
Y2 - 25 February 2019 through 28 February 2019
ER -
