Although recent generative image compression methods have demonstrated
impressive potential in optimizing the rate-distortion-perception trade-off,
they still face the critical challenge of flexible rate adaption to diverse
compression necessities and scenarios. To overcome this challenge, this paper
proposes a Controllable Generative Image Compression framework, Control-GIC,
the first capable of fine-grained bitrate adaption across a broad spectrum
while ensuring high-fidelity and generality compression. We base Control-GIC on
a VQGAN framework representing an image as a sequence of variable-length codes
(i.e. VQ-indices), which can be losslessly compressed and exhibits a direct
positive correlation with the bitrates. Therefore, drawing inspiration from the
classical coding principle, we naturally correlate the information density of
local image patches with their granular representations, to achieve dynamic
adjustment of the code quantity following different granularity decisions. This
implies we can flexibly determine a proper allocation of granularity for the
patches to acquire desirable compression rates. We further develop a
probabilistic conditional decoder that can trace back to historic encoded
multi-granularity representations according to transmitted codes, and then
reconstruct hierarchical granular features in the formalization of conditional
probability, enabling more informative aggregation to improve reconstruction
realism. Our experiments show that Control-GIC allows highly flexible and
controllable bitrate adaption and even once compression on an entire dataset to
fulfill constrained bitrate conditions. Experimental results demonstrate its
superior performance over recent state-of-the-art methods.

提出了一种可控生成图像压缩框架 (Control-GIC)，首次实现了在广泛频谱范围内的精细比特率调整，确保高保真度和通用性压缩。基于 VQGAN 框架，将图像表示为可变长度代码的序列，通过与局部图像块的信息密度相关联，实现了动态调整代码数量的不同粒度决策，进而实现了理想的压缩速率。实验结果表明，Control-GIC 在高度灵活和可控比特率适应性方面表现出优越性能。

一次性可控生成图像压缩与动态粒度适应

Once-for-All: Controllable Generative Image Compression with Dynamic  Granularity Adaption

We introduce EGIC, a novel generative image compression method that allows
traversing the distortion-perception curve efficiently from a single model.
Specifically, we propose an implicitly encoded variant of image interpolation
that predicts the residual between a MSE-optimized and GAN-optimized decoder
output. On the receiver side, the user can then control the impact of the
residual on the GAN-based reconstruction. Together with improved GAN-based
building blocks, EGIC outperforms a wide-variety of perception-oriented and
distortion-oriented baselines, including HiFiC, MRIC and DIRAC, while
performing almost on par with VTM-20.0 on the distortion end. EGIC is simple to
implement, very lightweight (e.g. 0.18x model parameters compared to HiFiC) and
provides excellent interpolation characteristics, which makes it a promising
candidate for practical applications targeting the low bit range.

本研究提出了一种新的生成图像压缩方法：EGIC。该方法能够从单一模型有效地穿越失真 - 感知曲线，并提出了一种隐式编码的图像插值变体，通过预测均方误差优化和 GAN 优化解码器输出之间的残差来控制基于 GAN 的重建结果，性能优于多种以感知或失真为导向的比较方法，并几乎与失真端的 VTM-20.0 相媲美。EGIC 实现简单、非常轻量级，并具有出色的插值特性，是应用于低比特范围的实际应用中有很大潜力的候选方法。