Scalable Face Image Coding via StyleGAN Prior: Towards Compression for
  Human-Machine Collaborative Vision
Qi Mao Chongyu Wang Meng Wang Shiqi Wang Ruijie Chen Libiao Jin Siwei Ma
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.