  Entropy estimation is essential for the performance of learned image
compression. It has been demonstrated that a transformer-based entropy model is
of critical importance for achieving a high compression ratio, however, at the
expense of a significant computational effort. In this work, we introduce the
Efficient Contextformer (eContextformer) - a computationally efficient
transformer-based autoregressive context model for learned image compression.
The eContextformer efficiently fuses the patch-wise, checkered, and
channel-wise grouping techniques for parallel context modeling, and introduces
a shifted window spatio-channel attention mechanism. We explore better training
strategies and architectural designs and introduce additional complexity
optimizations. During decoding, the proposed optimization techniques
dynamically scale the attention span and cache the previous attention
computations, drastically reducing the model and runtime complexity. Compared
to the non-parallel approach, our proposal has ~145x lower model complexity and
~210x faster decoding speed, and achieves higher average bit savings on Kodak,
CLIC2020, and Tecnick datasets. Additionally, the low complexity of our context
model enables online rate-distortion algorithms, which further improve the
compression performance. We achieve up to 17% bitrate savings over the intra
coding of Versatile Video Coding (VVC) Test Model (VTM) 16.2 and surpass
various learning-based compression models.
