English(EN) Neural Image Compression with Internal-External Prior Fusion and Alignment

新型神经图像压缩模型IE-PFA实现SOTA性能

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-06 05:00

研究人员开发了一种新颖的神经图像压缩模型IE-PFA，它整合了内部图像先验和源自自然图像常见模式的外部先验。该模型利用基于RWKV的模块高效融合这些先验，并采用仅训练机制进行对齐，实现了最先进的率失真性能。IE-PFA支持64个质量级别的可变比特率编码，并且与现有方法相比，编码和解码速度显著提高，平均BD-Rate比VTM-22.0-Intra节省21.65%。 AI

影响该模型推动了神经图像压缩领域的发展，有望实现更高效的视觉数据存储和传输。

排序理由这是一篇详细介绍新型神经图像压缩模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Tiancheng Si, Wenlong Xu, Dahua Gao · 2026-11-06 05:00

基于内外先验融合与对齐的神经图像压缩

arXiv:2611.07644v1 Announce Type: cross Abstract: Existing context models for learned image com- pression rely primarily on internal priors from the current image, overlooking common patterns and typical textures shared across natural images. To address this limitation, we propos…

本文如何被排名

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基于内外先验融合与对齐的神经图像压缩

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