TY - GEN
T1 - An image compression framework with learning-based filter
AU - Sun, Heming
AU - Liu, Chao
AU - Katto, Jiro
AU - Fan, Yibo
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61674041, in part by Alibaba Group through Alibaba Innovative Research (AIR) Program, in part by the STCSM under Grant 16XD1400300, in part by the pioneering project of academy for engineering and technology and Fudan-CIOMP joint fund, in part by the National Natural Science Foundation of China under Grant 61525401the Program of Shanghai Academic/Technology Research Leader under Grant 16XD1400300, the Innovation Program of Shanghai Municipal Education Commission, in part by JST, PRESTO Grant Number JPMJPR19M5, Japan.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - In this paper, a coding framework VIP-ICT-Codec is introduced. Our method is based on the VTM (Versatile Video Coding Test Model). First, we propose a color space conversion from RGB to YUV domain by using a PCA-like operation. A method for the PCA mean calculation is proposed to de-correlate the residual components of YUV channels. Besides, the correlation of UV components is compensated considering that they share the same coding tree in VVC. We also learn a residual mapping to alleviate the over-filtered and under-filtered problem of specific images. Finally, we regard the rate control as an unconstraint Lagrangian problem to reach the target bpp. The results show that we achieve 32.625dB at the validation phase.
AB - In this paper, a coding framework VIP-ICT-Codec is introduced. Our method is based on the VTM (Versatile Video Coding Test Model). First, we propose a color space conversion from RGB to YUV domain by using a PCA-like operation. A method for the PCA mean calculation is proposed to de-correlate the residual components of YUV channels. Besides, the correlation of UV components is compensated considering that they share the same coding tree in VVC. We also learn a residual mapping to alleviate the over-filtered and under-filtered problem of specific images. Finally, we regard the rate control as an unconstraint Lagrangian problem to reach the target bpp. The results show that we achieve 32.625dB at the validation phase.
UR - http://www.scopus.com/inward/record.url?scp=85090121445&partnerID=8YFLogxK
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U2 - 10.1109/CVPRW50498.2020.00084
DO - 10.1109/CVPRW50498.2020.00084
M3 - Conference contribution
AN - SCOPUS:85090121445
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 602
EP - 606
BT - Proceedings - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020
PB - IEEE Computer Society
T2 - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020
Y2 - 14 June 2020 through 19 June 2020
ER -