In this work, we introduce Efficient Contextformer (eContextformer) for
context modeling in lossy learned image compression, which is built upon our
previous work, Contextformer. The eContextformer combines the recent
advancements in efficient transformers and fast context models with the
spatio-channel attention mechanism. The proposed model enables content-adaptive
exploitation of the spatial and channel-wise latent dependencies for a high
performance and efficient entropy modeling. By incorporating several
innovations, the eContextformer features improved decoding speed, model
complexity and rate-distortion performance over previous work. For instance,
compared to Contextformer, the eContextformer requires 145x less model
complexity, 210x less decoding speed and achieves higher average bit savings on
the Kodak, CLIC2020 and Tecnick datasets. Compared to the standard Versatile
Video Coding (VVC) Test Model (VTM) 16.2, the proposed model provides up to
17.1% bitrate savings and surpasses various learning-based models.

本文中，我们介绍了一种利用 Efficient Contextformer 对有损图像压缩中的上下文建模的方法，使其在模型复杂度、解码速度和码率失真性能上有所改进。

高效的 Contextformer：基于空间 - 通道窗口注意力的快速上下文模拟用于学习图像压缩

Efficient Contextformer: Spatio-Channel Window Attention for Fast  Context Modeling in Learned Image Compression

GAN-based image compression schemes have shown remarkable progress lately due
to their high perceptual quality at low bit rates. However, there are two main
issues, including 1) the reconstructed image perceptual degeneration in color,
texture, and structure as well as 2) the inaccurate entropy model. In this
paper, we present a novel GAN-based image compression approach with improved
rate-distortion optimization (RDO) process. To achieve this, we utilize the
DISTS and MS-SSIM metrics to measure perceptual degeneration in color, texture,
and structure. Besides, we absorb the discretized gaussian-laplacian-logistic
mixture model (GLLMM) for entropy modeling to improve the accuracy in
estimating the probability distributions of the latent representation. During
the evaluation process, instead of evaluating the perceptual quality of the
reconstructed image via IQA metrics, we directly conduct the Mean Opinion Score
(MOS) experiment among different codecs, which fully reflects the actual
perceptual results of humans. Experimental results demonstrate that the
proposed method outperforms the existing GAN-based methods and the
state-of-the-art hybrid codec (i.e., VVC).

本文提出了一种改进型的 GAN 图像压缩方法，通过使用 DISTS 和 MS-SSIM 指标对颜色、纹理和结构的感知退化度量以及吸收离散化的高斯 - 拉普拉斯 - 逻辑混合模型（GLLMM）来改善熵模型的准确性，并采用 MOS 实验直接评估重构图像的感知质量，实验结果表明该方法优于现有的 GAN 方法和 VVC 等最先进的混合编解码器。

基于 GAN 的图像压缩及改进的 RDO 处理

GAN-based Image Compression with Improved RDO Process

Entropy modeling is a key component for high-performance image compression
algorithms. Recent developments in autoregressive context modeling helped
learning-based methods to surpass their classical counterparts. However, the
performance of those models can be further improved due to the underexploited
spatio-channel dependencies in latent space, and the suboptimal implementation
of context adaptivity. Inspired by the adaptive characteristics of the
transformers, we propose a transformer-based context model, named
Contextformer, which generalizes the de facto standard attention mechanism to
spatio-channel attention. We replace the context model of a modern compression
framework with the Contextformer and test it on the widely used Kodak,
CLIC2020, and Tecnick image datasets. Our experimental results show that the
proposed model provides up to 11% rate savings compared to the standard
Versatile Video Coding (VVC) Test Model (VTM) 16.2, and outperforms various
learning-based models in terms of PSNR and MS-SSIM.

提出了一种基于 transformers 的上下文模型 Contextformer，在学习压缩图像的过程中，该模型可以泛化到高维空间，并通过引入空间信道关注机制，从而进一步提高上下文相关性和适应性。与其他基于学习的模型相比，在 Kodak、CLIC2020 和 Tecnick 图像数据集上对比实验结果表明，提出的模型比其他方法实现更高的图像压缩效率，并取得了较高的 PSNR 和 MS-SSIM 分数。