# Reduced-complexity Adaptive Loop Filtering via Input-dependent Graph Filters

Source: [Reduced-complexity Adaptive Loop Filtering via Input-dependent Graph Filters](<https://www.sota2.com/research/paper/6a4c884085dba69fef8b2dec>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-07-08T05:00:28.404Z

Authors: Wen-Yang Lu, Eduardo Pavez, Antonio Ortega, Roman Chernyak, Shan Liu

Published: 2026-07-06T12:21:33.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Adaptive Loop Filtering on BVI-DVC (five-fold cross-val)](<https://www.sota2.com/research/sota/adaptive-loop-filtering-on-bvi-dvc-five-fold-cross-val.md>): Adaptive Loop Filtering; BVI-DVC (five-fold cross-val); MAE: 20.89.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2607.04985>)

[PDF](<https://arxiv.org/pdf/2607.04985.pdf>)