Contextformer: A Transformer with Spatio-Channel Attention for Context Modeling in Learned Image Compression

• URL: http://arxiv.org/abs/2203.02452v1
• Date: Fri, 4 Mar 2022 17:29:32 GMT
• Title: Contextformer: A Transformer with Spatio-Channel Attention for Context Modeling in Learned Image Compression
• Authors: A. Burakhan Koyuncu, Han Gao, Eckehard Steinbach
• Abstract summary: We propose a transformer-based context model ak.a. Contextformer. 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% rate savings compared to the standard Versatile Video Coding (VVC) Test Model (VVC) 9.1.
• Score: 5.152019611975467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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% rate savings compared to the standard Versatile Video Coding (VVC) Test Model (VTM) 9.1, and outperforms various learning-based models.

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