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。

EGIC：由语义分割指导的增强低比特率生成图像压缩

EGIC: Enhanced Low-Bit-Rate Generative Image Compression Guided by  Semantic Segmentation

Single image super-resolution (SISR) aims to reconstruct high-resolution (HR)
images from the given low-resolution (LR) ones, which is an ill-posed problem
because one LR image corresponds to multiple HR images. Recently,
learning-based SISR methods have greatly outperformed traditional ones, while
suffering from over-smoothing, mode collapse or large model footprint issues
for PSNR-oriented, GAN-driven and flow-based methods respectively. To solve
these problems, we propose a novel single image super-resolution diffusion
probabilistic model (SRDiff), which is the first diffusion-based model for
SISR. SRDiff is optimized with a variant of the variational bound on the data
likelihood and can provide diverse and realistic SR predictions by gradually
transforming the Gaussian noise into a super-resolution (SR) image conditioned
on an LR input through a Markov chain. In addition, we introduce residual
prediction to the whole framework to speed up convergence. Our extensive
experiments on facial and general benchmarks (CelebA and DIV2K datasets) show
that 1) SRDiff can generate diverse SR results in rich details with
state-of-the-art performance, given only one LR input; 2) SRDiff is easy to
train with a small footprint; and 3) SRDiff can perform flexible image
manipulation including latent space interpolation and content fusion.

本文提出一种基于扩散概率模型的单幅图像超分辨率方法，通过优化数据可能性的变分下界进行模型训练，并通过引入残差预测以加速收敛。该方法在 CelebA 和 DIV2K 数据集上进行了实验验证，实验结果表明，该方法在性能上领先以往的方法，并具有生成丰富细节的不同超分辨率结果、小型化、灵活的图像操作等优点。