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English(EN) Mixture-of-Experts-based Entropy Model for Learned Image Compression

混合专家模型提升图像压缩效率

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的、基于混合专家（MoE）模型的学习图像压缩熵模型，称为 MoEE。该方法允许模型仅激活给定输入图像所需的参数，从而提高效率。在 Kodak 数据集上，MoEE 模型相比 Versatile Video Coding (VVC) 实现了 -16.85% 的显著 BD-Rate 提升。 AI

影响 这项研究可能带来更高效的图像压缩技术，影响视觉数据的存储和传输。

排序理由 该集群包含一篇详细介绍新型图像压缩模型的 ist 研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

混合专家模型提升图像压缩效率

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该集群包含一篇详细介绍新型图像压缩模型的 ist 研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Jonas Brenig, Radu Timofte ·

    基于混合专家模型的学习图像压缩熵模型

    arXiv:2608.10947v1 Announce Type: new Abstract: 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 Ex…

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