The accelerated proliferation of visual content and the rapid development of
machine vision technologies bring significant challenges in delivering visual
data on a gigantic scale, which shall be effectively represented to satisfy
both human and machine requirements. In this work, we investigate how
hierarchical representations derived from the advanced generative prior
facilitate constructing an efficient scalable coding paradigm for human-machine
collaborative vision. Our key insight is that by exploiting the StyleGAN prior,
we can learn three-layered representations encoding hierarchical semantics,
which are elaborately designed into the basic, middle, and enhanced layers,
supporting machine intelligence and human visual perception in a progressive
fashion. With the aim of achieving efficient compression, we propose the
layer-wise scalable entropy transformer to reduce the redundancy between
layers. Based on the multi-task scalable rate-distortion objective, the
proposed scheme is jointly optimized to achieve optimal machine analysis
performance, human perception experience, and compression ratio. We validate
the proposed paradigm's feasibility in face image compression. Extensive
qualitative and quantitative experimental results demonstrate the superiority
of the proposed paradigm over the latest compression standard Versatile Video
Coding (VVC) in terms of both machine analysis as well as human perception at
extremely low bitrates ($<0.01$ bpp), offering new insights for human-machine
collaborative compression.

利用 StyleGAN 先验，本研究探讨了利用派生的分层表示构建有效的可伸缩编码范式以满足人和机器的需求。通过逐步学习三层编码分层语义表示，构建机器智能和人类视觉感知支持的渐进式范式，并通过分层可伸缩熵转换器降低层间冗余，达到高效压缩目标。通过多任务可扩展率失真目标对提议的方案进行联合优化，证实在面部图像压缩方面，该范式优于最新的可变视频编码 (VVC) 标准，提供了人机协同压缩方面的新见解。