基于混合专家（Mixture-of-Experts，MoE）的学习型图像压缩熵模型
Mixture-of-Experts-based Entropy Model for Learned Image Compression
AI总结：
本文将混合专家（MoE）方法引入学习型图像压缩，提出MoEE熵模型，使模型可选择性激活所需参数子集，在Kodak数据集上较VVC实现-16.85%的BD-Rate提升，优化了学习型图像压缩性能。
AI中文摘要：
近年来，端到端学习模型的发展使学习型图像压缩取得显著进展，其压缩效率优于现有最优传统方法。近期，混合专家（Mixture of Experts，MoE）方法在自然语言处理和计算机视觉任务中展现出良好效果。本文将MoE方法引入学习型图像压缩，提出用于学习型图像压缩的基于MoE的熵模型（MoEE），使模型能选择性激活输入图像所需的参数子集。该模型在Kodak数据集上相对于VVC实现了-16.85%的BD-Rate提升。
英文摘要：
Learned image compression has seen significant progress in recent years with the development of end-to-end learned models that achieve better compression efficiency than state-of-the-art conventional methods. Recently, Mixture of Experts (MoE) approaches have seen promising results in NLP and computer vision tasks. In this paper, we introduce the MoE approach to learned image compression. We propose a MoE-based Entropy model (MoEE) for learned image compression, allowing the model to selectively activate only the subset of parameters required for the input image. Our model achieves a BD-Rate improvement over VVC of -16.85% on the Kodak dataset.
