Scalable Facial Image Compression with Deep Feature Reconstruction
This addresses compression for surveillance applications, but appears incremental as it combines existing compress-then-analyze and analyze-then-compress approaches.
The paper tackles facial image compression by proposing a scalable scheme with base and enhancement layers for feature and texture representation, achieving better rate-accuracy and rate-distortion performance compared to conventional methods.
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.