  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.
