新的 BLI 框架推动渐进式图像压缩发展

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

研究人员开发了一种名为 Bound-Conditioned Latent Inference (BLI) 的新型渐进式图像压缩框架。该方法将三元组的解码不仅视为离散符号，而且视为连续潜在变量上的区间约束。通过使用这些不断缩小的区间，BLI 改进了熵模型和潜在估计的高斯参数，从而提高了率失真性能。实验表明，BLI 在标准数据集上优于 DPICT 和 CTC 等现有渐进式编解码器，使用的参数更少，延迟更低。 AI

影响推动了图像压缩技术的发展，可能提高 AI 生成或处理图像的效率。

排序理由这是一篇详细介绍图像压缩新技术框架的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Jaeseok Jang, Seungmin Jeon, Kwang Pyo Choi, Chang-Su Kim · 2026-10-23 04:00

Bound-Conditioned Latent Inference for Progressive Image Compression

arXiv:2610.30966v1 Announce Type: cross Abstract: Progressive image compression reconstructs images from prefixes of a single bitstream and therefore involves sequential latent inference under partial observations. Existing trit-plane codecs mainly treat decoded trits as discrete…

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Bound-Conditioned Latent Inference for Progressive Image Compression

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