2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDCOM-2.4
Paper Title GRAPH-CONVOLUTION NETWORK FOR IMAGE COMPRESSION
Authors Chunhui Yang, Yi Ma, Jiayu Yang, Shiyi Liu, Ronggang Wang, Shenzhen Graduate School of Peking University, China
SessionCOM-2: Learning-based Image and Video Coding
LocationArea H
Session Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Poster
Topic Image and Video Communications: Lossy coding of images & video
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Currently, convolution neural network is widely applied in image compression frameworks. However, classical convolution can only capture local information because of the heavy restriction of the fixed-shape receptive field. In this paper, we propose a novel image compression network, which introduces graph convolution block (GCB) to enhance the capability of extracting image information in the encoder. In GCB, the graph convolution and residual block are utilized to acquire local and global image features at the same time. Furthermore, an effective dequantization strategy is developed so that the decoder can learn better parameters to reconstruct more image information that is lost in quantization. Extensive experiments demonstrate that our model has outstanding performance, which outperforms existing excellent classical and learned image compression frameworks.