Learning-based image compression methods have made great progress. Most of them are designed for generic natural images. In fact, low-light images frequently occur due to unavoidable environmental influences or technical limitations, such as insufficient lighting or limited exposure time. %When general-purpose image compression algorithms compress low-light images, useful detail information is lost, resulting in a dramatic decrease in image enhancement. Once low-light images are compressed by existing general image compression approaches, useful information(e.g., texture details) would be lost resulting in a dramatic performance decrease in low-light image enhancement. To simultaneously achieve a higher compression rate and better enhancement performance for low-light images, we propose a novel image compression framework with joint optimization of low-light image enhancement. We design an end-to-end trainable two-branch architecture with lower computational cost, which includes the main enhancement branch and the signal-to-noise ratio~(SNR) aware branch. Experimental results show that our proposed joint optimization framework achieves a significant improvement over existing ``Compress before Enhance" or ``Enhance before Compress" sequential solutions for low-light images. Source codes are included in the supplementary material.

翻译：基于学习的图像压缩方法已取得巨大进展。现有方法大多针对通用自然图像设计。然而，由于不可避免的环境影响或技术限制（如光照不足或曝光时间有限），低光照图像频繁出现。当通用图像压缩算法压缩低光照图像时，有用的细节信息会丢失，导致图像增强效果显著下降。为同时实现低光照图像更高的压缩率和更好的增强性能，我们提出了一种与低光照图像增强联合优化的新型图像压缩框架。我们设计了一种计算成本更低的端到端可训练双分支架构，包括主增强分支和信噪比（SNR）感知分支。实验结果表明，我们提出的联合优化框架相比现有的"先压缩后增强"或"先增强后压缩"顺序式解决方案，在低光照图像上取得了显著提升。源代码包含在补充材料中。