Abstract
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.
Abstract (translated)
本文提出了一种可扩展的图像压缩方案，包括特征表示的基础层和纹理表示的增强层。更具体地说，为了进行分析，将底层设计为深度学习特征，并通过深度特征重构将其转化为精细结构。增强层用于压缩输入图像和从基础层生成的信号之间的残差，旨在忠实地重建输入纹理。该方案可以有效地继承压缩、分析、压缩两种方案在监控应用中的优点。通过人脸图像验证了该框架的性能，实验结果表明，该框架比传统的图像压缩方案具有更好的速率精度和速率失真性能。
URL
https://arxiv.org/abs/1903.05921
