Scalable Facial Image Compression with Deep Feature Reconstruction

TL;DR

This paper introduces a scalable facial image compression method combining deep feature analysis and residual texture encoding, achieving improved rate-distortion performance for surveillance images.

Contribution

It presents a novel deep feature-based scalable compression scheme that integrates analysis and reconstruction, outperforming traditional methods in rate-accuracy trade-offs.

Findings

Better rate-accuracy performance than conventional schemes

Effective reconstruction of facial textures from deep features

Applicable to surveillance image compression

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.