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.

本文介绍了一种自适应图像压缩变压器 (AICT) 框架，结合了更直接有效的 Transformer-based 通道自回归模型和可学习缩放模块。在基准数据集上的实验结果表明，AICT 框架在编解码效率和解码器复杂度之间的平衡方面显著优于 VVC 参考编码器和神经编解码器 SwinT-ChARM。

AICT：自适应图像压缩变压器

AICT: An Adaptive Image Compression Transformer

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.

该研究提出了一种改进的变压器 (Transformers) 自回归先验模型及 ConvNeXt-based 预 / 后处理器，并将其应用于图像压缩，提高了压缩效率和解码复杂度的权衡。