Peering into The Sketch: Ultra-Low Bitrate Face Compression for Joint Human and Machine Perception

摘要

We propose a novel face compression framework that leverages the external priors for joint human and machine perception under ultra-low bitrate scenarios. The proposed framework leverages the semantic richness of face images by representing the faces into sketches and thumbnails, resulting in improved bitrate utility for both human and machine vision. At the decoder side, the framework introduces a two-stage generative reconstruction, which faithfully enhances the reconstructed image via semi-parametric modeling and retrieved guidance from the external database. In particular, this coarse-to-fine strategy also results in improved identity consistency and analysis performance of the reconstructed image. Extensive evaluations of the proposed method have been conducted on the public face dataset by comparing it with end-to-end image compression techniques as well as traditional image compression standards. The experimental results demonstrate the effectiveness of the proposed method via superior perceptual and analytical performance under ultra-low bitrate conditions.