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Shanshe Wang
Researcher at Peking University
Publications - 180
Citations - 2388
Shanshe Wang is an academic researcher from Peking University. The author has contributed to research in topics: Computer science & Coding (social sciences). The author has an hindex of 18, co-authored 136 publications receiving 1188 citations. Previous affiliations of Shanshe Wang include City University of Hong Kong & Harbin Institute of Technology.
Papers
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Journal ArticleDOI
Image and Video Compression With Neural Networks: A Review
TL;DR: The evolution and development of neural network-based compression methodologies are introduced for images and video respectively and the joint compression on semantic and visual information is tentatively explored to formulate high efficiency signal representation structure for both human vision and machine vision.
Journal ArticleDOI
Content-Aware Convolutional Neural Network for In-Loop Filtering in High Efficiency Video Coding
TL;DR: This paper quantitatively analyzes the structure of the proposed CNN model from multiple dimensions to make the model interpretable and optimal for CNN-based loop filtering for high-efficiency video coding (HEVC).
Journal ArticleDOI
Rate-GOP Based Rate Control for High Efficiency Video Coding
TL;DR: Experimental results demonstrate the proposed Rate-GOP based rate control has much better R-D performance than the two state-of-the-art rate control schemes for HEVC.
Proceedings ArticleDOI
Recent Development of AVS Video Coding Standard: AVS3
TL;DR: A systematic and comprehensive overview of the third generation of Audio Video Standard (AVS) in China is presented, which has adopted many novel coding techniques including block partitioning structure, intra/inter and transform coding tools.
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Light Field Image Compression Using Generative Adversarial Network-Based View Synthesis
TL;DR: A LF image compression framework driven by a generative adversarial network (GAN)-based sub-aperture image (SAI) generation and a cascaded hierarchical coding structure that outperforms the state-of-the-art learning-based LFimage compression approach with on average 4.9% BD-rate reductions over multiple LF datasets.