Since HEVC intra rate control has no prior information to rely on for coding, it is a difficult work to obtain the optimal λ for every coding tree unit (CTU). In this paper, a convolutional neural network (CNN) based intra rate control is proposed. Firstly, a CNN with two last output channels is used to predict the key parameters of the CTU R-λ curve. For well training the CNN, a combining loss function is built and the balance factor γ is explored to achieve the minimum loss result. Secondly, the initial CTU λ can be calculated by the predicted results of the CNN and the allocated bit per pixel (bpp). According to the rate distortion optimization (RDO) of a frame, a spatial equation is derived between the CTU λ and the frame λ. Lastly, The CTU clipping function is used to obtain the optimal CTU λ for the intra rate control. The experimental results show that the proposed algorithm improves the intra rate control performance significantly with a good rate control accuracy.
Lili WEI
Shanghai University of Engineering Science
Zhenglong YANG
Shanghai University of Engineering Science
Zhenming WANG
Shanghai Branch of Communication Information Group Co., Ltd
Guozhong WANG
Shanghai University of Engineering Science
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Lili WEI, Zhenglong YANG, Zhenming WANG, Guozhong WANG, "A CNN-Based Optimal CTU λ Decision for HEVC Intra Rate Control" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 10, pp. 1766-1769, October 2021, doi: 10.1587/transinf.2021EDL8047.
Abstract: Since HEVC intra rate control has no prior information to rely on for coding, it is a difficult work to obtain the optimal λ for every coding tree unit (CTU). In this paper, a convolutional neural network (CNN) based intra rate control is proposed. Firstly, a CNN with two last output channels is used to predict the key parameters of the CTU R-λ curve. For well training the CNN, a combining loss function is built and the balance factor γ is explored to achieve the minimum loss result. Secondly, the initial CTU λ can be calculated by the predicted results of the CNN and the allocated bit per pixel (bpp). According to the rate distortion optimization (RDO) of a frame, a spatial equation is derived between the CTU λ and the frame λ. Lastly, The CTU clipping function is used to obtain the optimal CTU λ for the intra rate control. The experimental results show that the proposed algorithm improves the intra rate control performance significantly with a good rate control accuracy.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDL8047/_p
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@ARTICLE{e104-d_10_1766,
author={Lili WEI, Zhenglong YANG, Zhenming WANG, Guozhong WANG, },
journal={IEICE TRANSACTIONS on Information},
title={A CNN-Based Optimal CTU λ Decision for HEVC Intra Rate Control},
year={2021},
volume={E104-D},
number={10},
pages={1766-1769},
abstract={Since HEVC intra rate control has no prior information to rely on for coding, it is a difficult work to obtain the optimal λ for every coding tree unit (CTU). In this paper, a convolutional neural network (CNN) based intra rate control is proposed. Firstly, a CNN with two last output channels is used to predict the key parameters of the CTU R-λ curve. For well training the CNN, a combining loss function is built and the balance factor γ is explored to achieve the minimum loss result. Secondly, the initial CTU λ can be calculated by the predicted results of the CNN and the allocated bit per pixel (bpp). According to the rate distortion optimization (RDO) of a frame, a spatial equation is derived between the CTU λ and the frame λ. Lastly, The CTU clipping function is used to obtain the optimal CTU λ for the intra rate control. The experimental results show that the proposed algorithm improves the intra rate control performance significantly with a good rate control accuracy.},
keywords={},
doi={10.1587/transinf.2021EDL8047},
ISSN={1745-1361},
month={October},}
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TY - JOUR
TI - A CNN-Based Optimal CTU λ Decision for HEVC Intra Rate Control
T2 - IEICE TRANSACTIONS on Information
SP - 1766
EP - 1769
AU - Lili WEI
AU - Zhenglong YANG
AU - Zhenming WANG
AU - Guozhong WANG
PY - 2021
DO - 10.1587/transinf.2021EDL8047
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2021
AB - Since HEVC intra rate control has no prior information to rely on for coding, it is a difficult work to obtain the optimal λ for every coding tree unit (CTU). In this paper, a convolutional neural network (CNN) based intra rate control is proposed. Firstly, a CNN with two last output channels is used to predict the key parameters of the CTU R-λ curve. For well training the CNN, a combining loss function is built and the balance factor γ is explored to achieve the minimum loss result. Secondly, the initial CTU λ can be calculated by the predicted results of the CNN and the allocated bit per pixel (bpp). According to the rate distortion optimization (RDO) of a frame, a spatial equation is derived between the CTU λ and the frame λ. Lastly, The CTU clipping function is used to obtain the optimal CTU λ for the intra rate control. The experimental results show that the proposed algorithm improves the intra rate control performance significantly with a good rate control accuracy.
ER -