用语义分割指导多熵模型，提升无损图像压缩率
SEEC: Segmentation-Assisted Multi-Entropy Models for Learned Lossless Image Compression
- 根据语义区域选择不同熵模型，更精准建模像素分布
- 在基准数据集上达到顶尖压缩比，延迟几乎不变
- 支持重点区域编码，适合需要细节保留的应用
近年来，学习型图像压缩因其优于传统方法的性能而受到广泛关注。然而，现有方法大多使用单一熵模型对全图像素值的概率分布进行估计，难以捕捉不同语义区域的多样统计特性。为此，我们提出用于无损图像压缩的分割辅助多熵模型（SEEC）。该框架利用语义分割指导多个熵模型的选择与自适应，从而更准确地估计不同语义区域的概率分布。在基准数据集上的实验结果表明，SEEC在仅引入极小编码解码延迟的情况下，实现了当前最优的压缩率。此外，该模型还支持基于给定分割掩码的重点区域（ROIs）编码。代码已开源：https://github.com/chunbaobao/SEEC。
原文摘要 · Abstract (English)
Recently, learned image compression has attracted considerable attention due to its superior performance over traditional methods. However, most existing approaches employ a single entropy model to estimate the probability distribution of pixel values across the entire image, which limits their ability to capture the diverse statistical characteristics of different semantic regions. To overcome this limitation, we propose Segmentation-Assisted Multi-Entropy Models for Lossless Image Compression (SEEC). Our framework utilizes semantic segmentation to guide the selection and adaptation of multiple entropy models, enabling more accurate probability distribution estimation for distinct semantic regions. Experimental results on benchmark datasets demonstrate that SEEC achieves state-of-the-art compression ratios while introducing only minimal encoding and decoding latency. With superior performance, the proposed model also supports Regions of Interest (ROIs) coding condition on the provided segmentation mask. Our code is available at https://github.com/chunbaobao/SEEC.
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