Learned Image Compression with Channel-Wise Grouped Context Modeling

Liang Yuan, Jixiang Luo, Shaohui Li, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong

Abstract: Learned image compression has achieved improved rate-distortion performance with end-to-end optimized framework based on deep neural networks. However, context-based entropy modeling for learned image compression cannot simultaneously achieve enhanced efficiency and sufficiently exploiting the channel-wise correlations. In this paper, we propose a novel framework for learned image compression with channel-wise grouped context modeling. The proposed framework presents channel-wise grouping to explicitly exploit the channel-wise correlations and develop a grouped 3-D context model to achieve efficient entropy coding with a guarantee of rate-distortion performance. The proposed framework achieves competitive performance with a significantly reduced decoding complexity in comparison to 3-D context models.