提出分层渐进上下文模型，提升图像压缩的长程依赖建模效率。
Learned Image Compression with Hierarchical Progressive Context Modeling
- 分层编码调度逐级建模多尺度上下文依赖
- 渐进融合机制利用前步上下文信息，提升多样性建模
- 在压缩性能与计算开销间取得更优平衡
上下文建模在学习型图像压缩中对准确估计潜在变量分布至关重要。尽管现有先进方法已拓展了上下文建模能力，但仍难以高效利用不同编码步骤间的长程依赖和多样化上下文信息。本文提出一种新型分层渐进上下文模型（HPCM），以更高效地获取上下文信息。具体而言，HPCM采用分层编码调度，按序建模多尺度潜在变量间的上下文依赖，实现更高效的长程上下文建模；同时，设计渐进式上下文融合机制，将前一编码步骤的上下文信息融入当前步骤，有效利用多样化上下文。实验表明，该方法在率失真性能上达到当前最优，并在压缩效果与计算复杂度之间取得更好平衡。代码已开源：https://github.com/lyq133/LIC-HPCM。
原文摘要 · Abstract (English)
Context modeling is essential in learned image compression for accurately estimating the distribution of latents. While recent advanced methods have expanded context modeling capacity, they still struggle to efficiently exploit long-range dependency and diverse context information across different coding steps. In this paper, we introduce a novel Hierarchical Progressive Context Model (HPCM) for more efficient context information acquisition. Specifically, HPCM employs a hierarchical coding schedule to sequentially model the contextual dependencies among latents at multiple scales, which enables more efficient long-range context modeling. Furthermore, we propose a progressive context fusion mechanism that incorporates contextual information from previous coding steps into the current step, effectively exploiting diverse contextual information. Experimental results demonstrate that our method achieves state-of-the-art rate-distortion performance and strikes a better balance between compression performance and computational complexity. The code is available at https://github.com/lyq133/LIC-HPCM.
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