In the framework of learned image compression, the context model plays a
pivotal role in capturing the dependencies among latent representations. To
reduce the decoding time resulting from the serial autoregressive context
model, the parallel context model has been proposed as an alternative that
necessitates only two passes during the decoding phase, thus facilitating
efficient image compression in real-world scenarios. However, performance
degradation occurs due to its incomplete casual context. To tackle this issue,
we conduct an in-depth analysis of the performance degradation observed in
existing parallel context models, focusing on two aspects: the Quantity and
Quality of information utilized for context prediction and decoding. Based on
such analysis, we propose the \textbf{Corner-to-Center transformer-based
Context Model (C$^3$M)} designed to enhance context and latent predictions and
improve rate-distortion performance. Specifically, we leverage the
logarithmic-based prediction order to predict more context features from corner
to center progressively. In addition, to enlarge the receptive field in the
analysis and synthesis transformation, we use the Long-range Crossing Attention
Module (LCAM) in the encoder/decoder to capture the long-range semantic
information by assigning the different window shapes in different channels.
Extensive experimental evaluations show that the proposed method is effective
and outperforms the state-of-the-art parallel methods. Finally, according to
the subjective analysis, we suggest that improving the detailed representation
in transformer-based image compression is a promising direction to be explored.

在学习图像压缩框架中，上下文模型在捕捉潜在表示之间的依赖关系方面起到关键作用。为了减少串行自回归上下文模型导致的解码时间，提出了并行上下文模型作为一种替代方法，在解码阶段只需要两个传递，从而方便实现真实场景中的高效图像压缩。然而，由于其不完整的因果上下文，会导致性能下降。针对这个问题，我们对现有的并行上下文模型中观察到的性能下降进行了深入分析，着重从信息的数量和质量两个方面进行研究。基于此分析，我们提出了基于角到中心的变压器上下文模型 (C^3M)，旨在增强上下文和潜在预测，并提高速率 - 失真性能。具体来说，我们利用基于对数的预测顺序，逐步从角到中心预测更多的上下文特征。此外，为了扩大分析和合成变换中的感受野，我们在编码器 / 解码器中使用了长程交叉注意力模块 (LCAM)，通过在不同通道中分配不同的窗口形状来捕捉远程语义信息。广泛的实验评估表明，所提出的方法是有效的，并且优于最先进的并行方法。最后，根据主观分析，我们建议在基于变压器的图像压缩中改进细节表示是一个值得探索的方向。

高效的学习图片压缩的 Corner-to-Center 长距离上下文模型

Corner-to-Center Long-range Context Model for Efficient Learned Image  Compression

This paper presents a Transformer-based image compression system that allows
for a variable image quality objective according to the user's preference.
Optimizing a learned codec for different quality objectives leads to
reconstructed images with varying visual characteristics. Our method provides
the user with the flexibility to choose a trade-off between two image quality
objectives using a single, shared model. Motivated by the success of
prompt-tuning techniques, we introduce prompt tokens to condition our
Transformer-based autoencoder. These prompt tokens are generated adaptively
based on the user's preference and input image through learning a prompt
generation network. Extensive experiments on commonly used quality metrics
demonstrate the effectiveness of our method in adapting the encoding and/or
decoding processes to a variable quality objective. While offering the
additional flexibility, our proposed method performs comparably to the
single-objective methods in terms of rate-distortion performance.

这篇论文介绍了一种基于 Transformer 的图像压缩系统，根据用户的偏好提供可变的图像质量目标，通过引入提示符令牌，使得自适应生成，学习和适应编码和 / 或解码过程的方法，在可变的质量目标下提高了压缩效果。