LANCE通过自适应上下文估计提升过拟合图像压缩效率
LANCE: Locally Adaptive Neural Context Estimation for Overfitted Image Compression
- 引入前向信号空间超先验，实现局部自适应熵模型
- 在Kodak和CLIC数据集上分别降低1.40%~2.99%的率失真代价
- 适合需要高精度图像压缩且关注内容感知建模的研究者
本文提出局部自适应神经上下文估计（LANCE），作为Cool-Chic等过拟合图像压缩（OIC）框架的新扩展。传统OIC方法依赖轻量级自回归网络与全局参数，难以应对非平稳图像统计特性。LANCE通过引入前向信号的空间超先验，实现熵模型的区域自适应。为减少开销，采用静态中值边缘检测器（MED）与轻量级学习上下文模型结合的预测编码方案。实验表明，在解码器复杂度606-1483 MAC/pixel范围内，LANCE在Kodak数据集上较Cool-Chic 4.0降低1.40%的BD-rate，CLIC 2020上降低1.97%；在低复杂度端，分别提升2.41%和2.99%。定性分析显示，学习得到的空间超先验能有效将图像区域划分为具有相似统计特性的块，形成自动化的、内容感知的适应层。
原文摘要 · Abstract (English)
This paper introduces Locally Adaptive Neural Context Estimation (LANCE), a novel extension for overfitted image compression (OIC) frameworks like Cool-Chic. While traditional OIC methods rely on lightweight autoregressive networks with globally signaled parameters, they struggle with non-stationary image statistics. LANCE addresses this by incorporating a forward-signaled spatial hyperprior that enables regional adaptation of the entropy model. To minimize overhead, we employ a predictive coding scheme that combines a static Median Edge Detector (MED) with a lightweight learned context model. Experiments demonstrate that LANCE achieves BD-rate reductions of 1.40% on the Kodak dataset and 1.97% on CLIC 2020 over Cool-Chic 4.0 at the high end of our decoder complexity range of 606-1483 MAC/pixel. At the low end of the complexity range, we outperform Cool-Chic 4.0 by 2.41% and 2.99% on Kodak and CLIC, respectively. Qualitative analysis reveals that the learned spatial hyperprior effectively segments image regions into areas of similar image statistics, providing an automated, content-aware adaptation layer.
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