Semantic Scalable Image Compression with Cross-Layer Priors

In an intelligent society, image compression needs to serve both human vision and machine vision. Traditional image compression schemes only consider visual quality for humans. In addition, the bitstream needs to be fully decoded to images before performing semantic analysis (e.g., by deep neural networks). These two factors make traditional image compression schemes semantically inefficient. To better serve the needs of both human vision and machine vision, it is more reasonable to compress and transmit image signals and features simultaneously. In this paper, we propose a novel end-to-end semantic scalable image compression method, which progressively compresses coarse-grained semantic features, fine-grained semantic features, and image signals. To utilize the cross-layer correlation between features and image signals, we propose a cross-layer context model to reduce the information redundancy, which takes higher-layer features as cross-layer priors to predict the probability distribution parameters for the entropy model of lower-layer features or images. Furthermore, we adopt a Region of Interest (ROI) compression scheme. The objects with rich semantic information and the background are compressed separately, to further improve the compression efficiency. Experimental results on the CUB-200-2011 and FGVC-Aircraft datasets demonstrate the effectiveness of our proposed scheme compared to separate compression of image signals and features.