Recently, a decoupled framework for learning-based image compression has been proposed and adopted into the JPEG AI image coding standard developed by ISO/IEC WG1. The decoupled structure disentangles the sample reconstruction process and the entropy decoding process, making the decoding extremely fast. The corresponding techniques constitute the essential parts of the JPEG AI verification model software. However, its analysis transform and synthesis transform are relatively simple, which are built with stacked convolution layers, thereby may lack the capability to interpret data correlations. In this work, we enhance the transform networks by introducing the non-local attention mechanism, which has proven efficient in image compression tasks. The proposed framework thus shares the merits of the fast decoding from the decoupled architecture and the strong transform capabilities from the non-local …
本文针对学习型图像压缩中的解耦框架，该框架将样本重建与熵解码分离，实现极快解码，并成为JPEG AI验证模型的核心。然而其分析/合成变换仅由堆叠卷积构成，缺乏数据相关性建模能力。作者引入非局部注意力机制增强变换网络，使模型兼具解耦架构的快速解码优势与非局部注意力的强变换能力，提升了压缩性能。