TY - GEN
T1 - Deep convolutional network based image quality enhancement for low bit rate image compression
AU - Jia, Chuanmin
AU - Zhang, Xiang
AU - Zhang, Jian
AU - Wang, Shiqi
AU - Ma, Siwei
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/1/4
Y1 - 2017/1/4
N2 - In this contribution, a novel image quality enhancement algorithm based on convolutional network is proposed for low bit rate image compression. Specifically, a downsample procedure is performed to generate lower resolution image for low bit rate compression. While the decoder side, upsample is to be performed firstly to the original resolution. Image quality is further enhanced by the proposed convolutional deep network. In particular, an optional image quality improvement network can be utilized for further enhancement after the first network. With the help of deep network, more detailed and high-frequency information can be recovered while maintaining the consistency of contour area, leading to better visual quality. Another benefit of this approach lies in that the proposed approach is fully compatible with all third-party image codec pipeline. Experimental result shows that the proposed scheme significantly outperforms JPEG in low bit rate image compression.
AB - In this contribution, a novel image quality enhancement algorithm based on convolutional network is proposed for low bit rate image compression. Specifically, a downsample procedure is performed to generate lower resolution image for low bit rate compression. While the decoder side, upsample is to be performed firstly to the original resolution. Image quality is further enhanced by the proposed convolutional deep network. In particular, an optional image quality improvement network can be utilized for further enhancement after the first network. With the help of deep network, more detailed and high-frequency information can be recovered while maintaining the consistency of contour area, leading to better visual quality. Another benefit of this approach lies in that the proposed approach is fully compatible with all third-party image codec pipeline. Experimental result shows that the proposed scheme significantly outperforms JPEG in low bit rate image compression.
KW - Deep Convolutional Network
KW - Image Compression
KW - Low Bit Rate
UR - https://www.scopus.com/pages/publications/85011062095
U2 - 10.1109/VCIP.2016.7805504
DO - 10.1109/VCIP.2016.7805504
M3 - 会议稿件
AN - SCOPUS:85011062095
T3 - VCIP 2016 - 30th Anniversary of Visual Communication and Image Processing
BT - VCIP 2016 - 30th Anniversary of Visual Communication and Image Processing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE Visual Communication and Image Processing, VCIP 2016
Y2 - 27 November 2016 through 30 November 2016
ER -