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

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.