TY - GEN
T1 - Convolutional Neural Networks Based Intra Prediction for HEVC
AU - Cui, Wenxue
AU - Zhang, Tao
AU - Zhang, Shengping
AU - Jiang, Feng
AU - Zuo, Wangmeng
AU - Wan, Zhaolin
AU - Zhao, Debin
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/5/8
Y1 - 2017/5/8
N2 - Traditional intra prediction methods for HEVC rely on using the nearest reference lines for predicting a block, which ignore much richer context between the current block and its neighboring blocks and therefore cause inaccurate prediction especially when weak spatial correlation exists between the current block and the reference lines. To overcome this problem, in this paper, an intra-prediction convolutional neural network (IPCNN) is proposed for intra prediction, which exploits the rich context of the current block and therefore is capable of improving the accuracy of predicting the current block. Meanwhile, the reconstruction of the three nearest blocks can also be refined. To the best of our knowledge, this is the first paper that directly applies CNNs to intra prediction for HEVC. Experimental results validate the effectiveness of applying CNNs to intra prediction and the proposed method can achieve 0.70% bitrate reduction compared to HEVC reference software HM-14.0.
AB - Traditional intra prediction methods for HEVC rely on using the nearest reference lines for predicting a block, which ignore much richer context between the current block and its neighboring blocks and therefore cause inaccurate prediction especially when weak spatial correlation exists between the current block and the reference lines. To overcome this problem, in this paper, an intra-prediction convolutional neural network (IPCNN) is proposed for intra prediction, which exploits the rich context of the current block and therefore is capable of improving the accuracy of predicting the current block. Meanwhile, the reconstruction of the three nearest blocks can also be refined. To the best of our knowledge, this is the first paper that directly applies CNNs to intra prediction for HEVC. Experimental results validate the effectiveness of applying CNNs to intra prediction and the proposed method can achieve 0.70% bitrate reduction compared to HEVC reference software HM-14.0.
UR - https://www.scopus.com/pages/publications/85020006943
U2 - 10.1109/DCC.2017.53
DO - 10.1109/DCC.2017.53
M3 - 会议稿件
AN - SCOPUS:85020006943
T3 - Data Compression Conference Proceedings
SP - 436
BT - Proceedings - DCC 2017, 2017 Data Compression Conference
A2 - Bilgin, Ali
A2 - Serra-Sagrista, Joan
A2 - Marcellin, Michael W.
A2 - Storer, James A.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 Data Compression Conference, DCC 2017
Y2 - 4 April 2017 through 7 April 2017
ER -