Abstract
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.
Abstract (translated)
基于学习的图像压缩方法已经取得了巨大的进展。大部分设计用于通用自然图像。实际上,暗态图像经常由于不可避免的环境影响或技术限制,如光线不足或曝光时间有限,而发生。当通用图像压缩算法压缩暗态图像时,有用的细节信息被丢失,导致图像增强效果急剧下降。一旦暗态图像通过现有的通用图像压缩方法压缩,有用的信息(如纹理细节)将丢失,导致暗态图像增强效果的性能急剧下降。为了同时实现较高的压缩率和更好的增强性能,我们对暗态图像提出一种新的图像压缩框架,同时优化暗态图像增强。我们设计了一种 end-to-end 可训练的二分支架构,计算成本较低,其中包括主要增强分支和信号-噪声比~(SNR) aware分支。实验结果表明,我们提出的联合优化框架在暗态图像增强方面实现了与现有“压缩前增强”或“增强前压缩”方案的显著改进。源代码已包括补充材料。
URL
https://arxiv.org/abs/2305.15030