  Motivated by the efficiency investigation of the Tranformer-based transform
coding framework, namely SwinT-ChARM, we propose to enhance the latter, as
first, with a more straightforward yet effective Tranformer-based channel-wise
auto-regressive prior model, resulting in an absolute image compression
transformer (ICT). Current methods that still rely on ConvNet-based entropy
coding are limited in long-range modeling dependencies due to their local
connectivity and an increasing number of architectural biases and priors. On
the contrary, the proposed ICT can capture both global and local contexts from
the latent representations and better parameterize the distribution of the
quantized latents. Further, we leverage a learnable scaling module with a
sandwich ConvNeXt-based pre/post-processor to accurately extract more compact
latent representation while reconstructing higher-quality images. Extensive
experimental results on benchmark datasets showed that the proposed adaptive
image compression transformer (AICT) framework significantly improves the
trade-off between coding efficiency and decoder complexity over the versatile
video coding (VVC) reference encoder (VTM-18.0) and the neural codec
SwinT-ChARM.
