TY - GEN
T1 - Efficient CTU-based intra frame coding for HEVC based on deep learning
AU - Zhang, Zheng Teng
AU - Yeh, Chia Hung
AU - Kang, Li Wei
AU - Lin, Min Hui
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - To further improve the compression efficiency of HEVC intra frame coding, in this paper, a deep learning-based framework is proposed. Inspired by recently developed deep learning models for image super-resolution (SR), we propose to train a CNN (convolutional neural network) model to precisely predict the residual information of each CTU (coding tree unit) at the HEVC encoder. As a result, better CTU reconstruction and better prediction for the compression of subsequent CTUs can be achieved. To reduce computational complexity, different from current CNN-based SR works, we propose to skip the non-linear mapping layer, and incorporate the residual learning to obtain better predicted residual for CTU encoding. Experimental results have shown that the proposed method achieves 3.2% bitrate reduction in average BDBR (Bjentegaard delta bit rate) with only 37% encoding complexity increased.
AB - To further improve the compression efficiency of HEVC intra frame coding, in this paper, a deep learning-based framework is proposed. Inspired by recently developed deep learning models for image super-resolution (SR), we propose to train a CNN (convolutional neural network) model to precisely predict the residual information of each CTU (coding tree unit) at the HEVC encoder. As a result, better CTU reconstruction and better prediction for the compression of subsequent CTUs can be achieved. To reduce computational complexity, different from current CNN-based SR works, we propose to skip the non-linear mapping layer, and incorporate the residual learning to obtain better predicted residual for CTU encoding. Experimental results have shown that the proposed method achieves 3.2% bitrate reduction in average BDBR (Bjentegaard delta bit rate) with only 37% encoding complexity increased.
UR - http://www.scopus.com/inward/record.url?scp=85050818645&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050818645&partnerID=8YFLogxK
U2 - 10.1109/APSIPA.2017.8282116
DO - 10.1109/APSIPA.2017.8282116
M3 - Conference contribution
AN - SCOPUS:85050818645
T3 - Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
SP - 661
EP - 664
BT - Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
Y2 - 12 December 2017 through 15 December 2017
ER -