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显著进展，但多数方法针对通用自然图像设计。由于不可避免的环境影响或技术限制（如光照不足、曝光时间受限等），低光照图像频繁出现。当通用图像压缩算法对低光照图像进行压缩时，有用细节信息会丢失，导致图像增强性能急剧下降。为同时实现低光照图像的高压缩率与更优增强效果，我们提出了一种联合优化低光照图像增强的新型图像压缩框架。我们设计了一种端到端可训练的双分支架构，计算成本较低，包含主增强分支和信噪比感知分支。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"的顺序处理方案，在低光照图像上实现了显著性能提升。源代码包含在补充材料中。