Leveraging Conv-Attention for Efficient and High-Quality JPEG AI Image Coding

In this paper, we present a Conv-Attention, a decoder-friendly attention mechanism, in an effort to advancing the practical application of the artificial intelligence-based image coding. More specifically, the proposed method is tailored for JPEG AI, which is the latest advanced neural-network based image coding standard. By identifying the obstacles by profiling the decoding complexity of JPEG AI, the attention module accounts for a significant proportion, which mainly attributes to the intricate network structure and involvement of less efficient operations. Conv-Attention model is composed with plain convolution and activation computations, equipping with sub-scaling and up-scaling design, such that the non-adjacent features can be well captured, leading to the reduction of decoding complexity and maintenance of the synthesis and attentive capability. Simulation results verify the effectiveness of the proposed method with JPEG AI reference software, wherein the decoding complexity is reduced by 80% with negligible coding performance loss. The proposed method was adopted in the 100th JPEG meeting.