Contextformer: A Transformer with Spatio-Channel Attention for Context Modeling in Learned Image Compression
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Entropy modeling is a key component for high-performance image compression algorithms. Recent developments in autoregressive context modeling helped learning-based methods to surpass their classical counterparts. However, the performance of those models can be further improved due to the underexploited spatio-channel dependencies in latent space, and the suboptimal implementation of context adaptivity. Inspired by the adaptive characteristics of the transformers, we propose a transformer-based context model, a.k.a. Contextformer, which generalizes the de facto standard attention mechanism to spatio-channel attention. We replace the context model of a modern compression framework with the Contextformer and test it on the widely used Kodak image dataset. Our experimental results show that the proposed model provides up to 10 Model (VTM) 9.1, and outperforms various learning-based models.
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