Efficient Contextformer: Spatio-Channel Window Attention for Fast Context Modeling in Learned Image Compression

In this work, we introduce Efficient Contextformer (eContextformer) for context modeling in lossy learned image compression, which is built upon our previous work, Contextformer. The eContextformer combines the recent advancements in efficient transformers and fast context models with the spatio-channel attention mechanism. The proposed model enables content-adaptive exploitation of the spatial and channel-wise latent dependencies for a high performance and efficient entropy modeling. By incorporating several innovations, the eContextformer features improved decoding speed, model complexity and rate-distortion performance over previous work. For instance, compared to Contextformer, the eContextformer requires 145x less model complexity, 210x less decoding speed and achieves higher average bit savings on the Kodak, CLIC2020 and Tecnick datasets. Compared to the standard Versatile Video Coding (VVC) Test Model (VTM) 16.2, the proposed model provides up to 17.1% bitrate savings and surpasses various learning-based models.