基于StyleGAN先验的可扩展人脸图像编码：面向人机协同视觉的压缩
Scalable Face Image Coding via StyleGAN Prior: Towards Compression for Human-Machine Collaborative Vision
AI总结：
利用StyleGAN先验构建三层可扩展编码，通过逐层熵变换器减少冗余，实现极低比特率下优于VVC的人机协同人脸图像压缩。
摘要：
视觉内容的加速激增和机器视觉技术的快速发展，给大规模视觉数据的传输带来了重大挑战，这些数据需要被有效表示以满足人类和机器的需求。在本工作中，我们研究了如何利用先进生成先验中的层次化表示来构建一种高效的可扩展编码范式，以支持人机协同视觉。我们的关键洞察在于，通过利用StyleGAN先验，我们可以学习编码层次语义的三层表示，这些表示被精心设计为基础层、中间层和增强层，以渐进方式支持机器智能和人类视觉感知。为实现高效压缩，我们提出了逐层可扩展熵变换器以减少层间冗余。基于多任务可扩展率失真目标，所提方案被联合优化以实现最优的机器分析性能、人类感知体验和压缩比。我们验证了该范式在人脸图像压缩中的可行性。大量的定性和定量实验结果表明，在极低比特率（<0.01 bpp）下，所提范式在机器分析和人类感知方面均优于最新的压缩标准Versatile Video Coding（VVC），为人机协同压缩提供了新的见解。
英文摘要：
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.
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