用图结构提升视频去压缩伪影效果，滤波器数量减少十倍。
Reduced-complexity Adaptive Loop Filtering via Input-dependent Graph Filters
- 通过像素强度关系建图，捕捉局部结构信息
- 滤波器数量减少一个数量级，性能与基准相当
- 适合需要轻量化视频编码的场景
自适应环路滤波是现代视频编码中抑制压缩伪影的重要工具。在增强压缩模型（ECM）中，离线训练的固定滤波器通过细粒度梯度分类器实现高信号自适应性，但导致大量固定滤波器，带来冗余和实现复杂度上升。本文提出一种基于图的固定滤波框架，利用图编码像素强度关系，比仅依赖梯度分类更有效捕捉局部结构信息。固定滤波器被学习为多项式图滤波器，使结构相似的局部模式共享相同滤波行为。实验表明，该方法在保持与ECM基线相当性能的同时，将所需滤波器数量减少一个数量级。
原文摘要 · Abstract (English)
Adaptive Loop Filtering is an important tool for suppressing compression artifacts in modern video codecs. In the enhanced compression model (ECM), a software test model used for experimenting with video coding tools beyond Versatile Video Coding, fixed filters are trained offline and achieve high signal adaptivity via a fine-grained gradient-based classifier, resulting in a large number of fixed filters that introduce redundancy and increased implementation complexity. Reducing this redundancy without compromising artifact suppression, therefore, remains a key challenge. This paper proposes an alternative graph-based fixed-filtering framework for adaptive loop filtering. By using a graph to encode pixel-intensity relationships, our approach captures local structural information more effectively than gradient-based classification alone. Fixed filters are learned as polynomial graph filters, enabling structurally similar local patterns to share common filtering behavior. Experimental results demonstrate that the proposed approach achieves a comparable performance to the ECM baseline while reducing the number of required filters by an order of magnitude.
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