C-CTX: Cubic-Checkerboard Context Entropy Model for Learned Image Compression

Learned Image Compression (LIC) has achieved superior performance in recent years, of which the context entropy model is an important component. However, in the context entropy model, there is no deterministic correlation between neighboring channels, and it is difficult to capture inter-channel correlation as well as spatial correlation for further improving the performance. To address this issue, a Cubic-Checkerboard conTeXt entropy model (C-CTX) for LIC is proposed in this work, which is able to refer uniformly across the channel domain and maintain the correlations in the spatial domain. To make neighboring channels have more similar distribution, Cubic Checkerboard Mask (CCM) with channel-wise mask convolution is utilized to achieve uniform distribution in different domains and Channel Wise Re-Arrangement (CWRA) is performed in terms of entropy. Based on CCM and CWRA, two Feature Disentangle Modules (FDMs) are designed in C-CTX to project the context information within sub-spaces for catching spatial correlation and channel correlation separately. Extensive experimental evaluations show that our method outperforms the state-of-the-art works on six datasets, i.e., Kodak, Tecnick, CLIC'20, CLIC'21, CLIC'22, and JPEG-AI.

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