In this paper, we propose a scalable image compression scheme, including the
base layer for feature representation and enhancement layer for texture
representation. More specifically, the base layer is designed as the deep
learning feature for analysis purpose, and it can also be converted to the fine
structure with deep feature reconstruction. The enhancement layer, which serves
to compress the residuals between the input image and the signals generated
from the base layer, aims to faithfully reconstruct the input texture. The
proposed scheme can feasibly inherit the advantages of both
compress-then-analyze and analyze-then-compress schemes in surveillance
applications. The performance of this framework is validated with facial
images, and the conducted experiments provide useful evidences to show that the
proposed framework can achieve better rate-accuracy and rate-distortion
performance over conventional image compression schemes.

本文提出了一种可扩展的图像压缩方案，包括特征表示和纹理表示的基本层和增强层，其通过神经网络进行分析和深层特征重建，可以在视频监控应用中实现优秀的性能表现。而实验证明该框架可以在保证精度和失真率的情况下，比传统的图像压缩方案具有更好的性能。

使用深度特征重建的可扩展面部图像压缩

Scalable Facial Image Compression with Deep Feature Reconstruction

Deep learning has revolutionized many computer vision fields in the last few
years, including learning-based image compression. In this paper, we propose a
deep semantic segmentation-based layered image compression (DSSLIC) framework
in which the semantic segmentation map of the input image is obtained and
encoded as the base layer of the bit-stream. A compact representation of the
input image is also generated and encoded as the first enhancement layer. The
segmentation map and the compact version of the image are then employed to
obtain a coarse reconstruction of the image. The residual between the input and
the coarse reconstruction is additionally encoded as another enhancement layer.
Experimental results show that the proposed framework outperforms the
H.265/HEVC-based BPG and other codecs in both PSNR and MS-SSIM metrics across a
wide range of bit rates in RGB domain. Besides, since semantic segmentation map
is included in the bit-stream, the proposed scheme can facilitate many other
tasks such as image search and object-based adaptive image compression.

本论文提出了一种基于深度语义分割的分层图像压缩 (DSSLIC) 框架，通过将输入图像的语义分割映射编码为比特流的基本层，并生成编码为第一个增强层的输入图像的紧凑表示，进而获得图像的粗略重构，并额外编码作为另一种增强层与输入图像的差值，该框架可以在 RGB 域的广泛比特率中在 PSNR 和 MS-SSIM 指标方面优于 H.265/HEVC-based BPG 和其他编解码器，并用于图像搜索和基于对象的自适应图像压缩等多项任务。