End-to-End Image Compression via Attention-Guided Information-Preserving Module

Deep learning-based end-to-end image compression has achieved significant compression performance in recent years. However, current learning-based image compression methods are designed considering the characteristic of RGB color space, which is not suitable for image compression in YUV 420 color space because of the variance between color formats. To achieve efficient image compression in YUV 420 color space, we propose an information-preserving compression framework using the attention mechanism. Specifically, we design an information-preserving module (IPM), where we utilize the dual-branch architecture to prevent changes in data distribution and propose the feature attention block (FAB) to preserve information. Furthermore, a cross-channel progressive enhancement (CPE) network is designed by taking advantage of the relations among different channels. Ex-perimental results show that the proposed framework outper-forms state-of-the-art compression standard Versatile Video Coding (VVC) with 2.52% BD-rate reduction on common test conditions (CTC) sequences on average.