High-resolution (HR) images are usually downscaled to low-resolution (LR) ones for better display and afterward upscaled back to the original size to recover details. Recent work in image rescaling formulates downscaling and upscaling as a unified task and learns a bijective mapping between HR and LR via invertible networks. However, in real-world applications (e.g., social media), most images are compressed for transmission. Lossy compression will lead to irreversible information loss on LR images, hence damaging the inverse upscaling procedure and degrading the reconstruction accuracy. In this paper, we propose the Self-Asymmetric Invertible Network (SAIN) for compression-aware image rescaling. To tackle the distribution shift, we first develop an end-to-end asymmetric framework with two separate bijective mappings for high-quality and compressed LR images, respectively. Then, based on empirical analysis of this framework, we model the distribution of the lost information (including downscaling and compression) using isotropic Gaussian mixtures and propose the Enhanced Invertible Block to derive high-quality/compressed LR images in one forward pass. Besides, we design a set of losses to regularize the learned LR images and enhance the invertibility. Extensive experiments demonstrate the consistent improvements of SAIN across various image rescaling datasets in terms of both quantitative and qualitative evaluation under standard image compression formats (i.e., JPEG and WebP).

翻译：高分辨率（HR）图像通常被降采样为低分辨率（LR）图像以优化显示，随后再上采样至原始尺寸以恢复细节。现有图像缩放工作将降采样与上采样视为统一任务，通过可逆网络学习HR与LR间的双射映射。然而，在社交媒体等实际应用中，多数图像需经压缩传输。有损压缩会导致LR图像产生不可逆信息损失，进而破坏反向上采样过程并降低重建精度。本文提出自对称可逆网络（SAIN）用于压缩感知图像缩放。为解决分布偏移问题，我们首先构建端到端非对称框架，分别为高质量与压缩LR图像建立独立的双射映射。基于该框架的经验分析，我们采用各向同性高斯混合模型对（包含降采样与压缩的）信息丢失分布进行建模，并提出增强可逆模块，通过单次前向传播生成高质量/压缩LR图像。此外，我们设计系列损失函数以正则化学习到的LR图像并增强可逆性。大量实验表明，在标准图像压缩格式（JPEG与WebP）下，SAIN在多种图像缩放数据集的定量与定性评估中均取得一致性提升。