In this age of information, images are a critical medium for storing and transmitting information. With the rapid growth of image data amount, visual compression and visual data perception are two important research topics attracting a lot attention. However, those two topics are rarely discussed together and follow separate research path. Due to the compact compressed domain representation offered by learning-based image compression methods, there exists possibility to have one stream targeting both efficient data storage and compression, and machine perception tasks. In this paper, we propose a layered generative image compression model achieving high human vision-oriented image reconstructed quality, even at extreme compression ratios. To obtain analysis efficiency and flexibility, a task-agnostic learning-based compression model is proposed, which effectively supports various compressed domain-based analytical tasks while reserves outstanding reconstructed perceptual quality, compared with traditional and learning-based codecs. In addition, joint optimization schedule is adopted to acquire best balance point among compression ratio, reconstructed image quality, and downstream perception performance. Experimental results verify that our proposed compressed domain-based multi-task analysis method can achieve comparable analysis results against the RGB image-based methods with up to 99.6% bit rate saving (i.e., compared with taking original RGB image as the analysis model input). The practical ability of our model is further justified from model size and information fidelity aspects.

翻译：在信息时代，图像是存储和传输信息的关键媒介。随着图像数据量的快速增长，视觉压缩与视觉数据感知成为两个备受关注的重要研究课题。然而，这两个课题鲜少被共同探讨，并遵循着独立的研究路径。由于基于学习的图像压缩方法能够提供紧凑的压缩域表示，因此存在实现兼顾高效数据存储与压缩及机器感知任务单一流程的可能性。本文提出一种分层生成图像压缩模型，即使在极高压缩比下也能实现面向人类视觉的高质量图像重建。为获得分析效率与灵活性，我们提出一种任务无关的基于学习的压缩模型，该模型与传统及基于学习的编解码器相比，在有效支持各类基于压缩域的分析任务的同时，保持了卓越的重建感知质量。此外，采用联合优化策略以获取压缩比、重建图像质量与下游感知性能之间的最佳平衡点。实验结果表明，我们提出的基于压缩域的多任务分析方法能够达到与基于RGB图像的方法相当的分析结果，同时节省高达99.6%的比特率（即与将原始RGB图像作为分析模型输入相比）。从模型规模和信息保真度方面进一步验证了该模型的实际应用能力。