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新研究推动了面向人类和机器感知的学习式图像压缩 · 跟踪3个来源

作者 PulseAugur 编辑部 · [5 个来源] ·

三篇新研究论文探讨了学习式图像压缩的进展。第一篇论文引入了一种具有多尺度的分层潜在表示，以改进熵建模，并在 Kodak 数据集上实现了比 VVC 低 17.9% 的 BD 率。第二篇论文通过将信息分散到不同数据包并使用双分支自回归结构来解决数据包丢失的弹性问题，在数据包丢失情况下显示出显著的 PSNR 增益和降低的方差。第三篇论文专注于面向机器感知的渐进式学习式图像压缩，通过新颖的适配器和自适应解码控制器来调整面向人类的编解码器，以保持强大的下游分类性能。 AI

影响 学习式图像压缩的这些进展可能带来更高效的数据传输和存储，尤其是在具有挑战性的网络条件下，这将使需要高保真图像数据以进行机器感知任务的应用受益。

排序理由 该集群包含三篇在 arXiv 上发表的关于学习式图像压缩技术的学术论文。

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新研究推动了面向人类和机器感知的学习式图像压缩 · 跟踪3个来源

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该集群包含三篇在 arXiv 上发表的关于学习式图像压缩技术的学术论文。
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报道来源 [5]

  1. arXiv cs.AI TIER_1 English(EN) · Yuefeng Zhang ·

    HAMP-LIC: 用于学习式图像压缩的 Hessian 感知混合精度训练后量化

    arXiv:2608.12239v1 Announce Type: cross Abstract: Use this plain-text version for the arXiv abstract field: Learned image compression (LIC) models achieve strong rate-distortion performance but are hindered by high computational complexity and encoding-decoding mismatches across …

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    HAMP-LIC: 用于学习型图像压缩的 Hessian 感知混合精度训练后量化

    Use this plain-text version for the arXiv abstract field: Learned image compression (LIC) models achieve strong rate-distortion performance but are hindered by high computational complexity and encoding-decoding mismatches across heterogeneous hardware platforms. Uniform fixed-pr…

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

    多尺度潜在表示用于学习型图像压缩

    arXiv:2608.10952v1 Announce Type: new Abstract: Most learned image compression systems rely on a single latent representation combined with a hyperprior, which limits their ability to efficiently capture image structure across spatial scales. In this work, we propose a hierarchic…

  4. arXiv cs.CV TIER_1 English(EN) · Yuhang Wei (Shanghai Jiao Tong University), Chuqin Zhou (Shanghai Jiao Tong University), Yibo Shi (Huawei Technologies Ltd), Jing Wang (Huawei Technologies Ltd), Guo Lu (Shanghai Jiao Tong University) ·

    每一比特都至关重要：面向抗损耗学习图像压缩的信息分散

    arXiv:2608.11096v1 Announce Type: new Abstract: Learned image compression (LIC) has achieved impressive rate-distortion performance. However, existing methods remain highly vulnerable to packet loss, a common challenge in satellite and emergency communications. This vulnerability…

  5. arXiv cs.CV TIER_1 English(EN) · Jungwoo Kim, Jun-Hyuk Kim, Jong-Seok Lee ·

    面向机器感知的渐进式学习图像压缩

    arXiv:2512.20070v2 Announce Type: replace Abstract: Recent advances in learned image codecs have extended from human perception toward machine perception However, progressive image compression with fine granular scalability (FGS)-which enables decoding a single bitstream at multi…

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