  Recently, the performance of neural image compression (NIC) has steadily
improved thanks to the last line of study, reaching or outperforming
state-of-the-art conventional codecs. Despite significant progress, current NIC
methods still rely on ConvNet-based entropy coding, limited in modeling
long-range dependencies due to their local connectivity and the increasing
number of architectural biases and priors, resulting in complex underperforming
models with high decoding latency. 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). Through the proposed ICT, we 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 codes while
reconstructing higher-quality images. Extensive experimental results on
benchmark datasets showed that the proposed 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. Moreover, we provide model scaling studies to verify the
computational efficiency of our approach and conduct several objective and
subjective analyses to bring to the fore the performance gap between the
adaptive image compression transformer (AICT) and the neural codec SwinT-ChARM.
