Parameter Adaptive Network for Large-Scale Neural In-Loop Filtering in Versatile Video Coding

Efficient in-loop filtering is critical for the latest video-coding standard, versatile video coding (H.266/VVC). While parameter-adaptive mechanisms are effective in traditional adaptive loop filters and overfitted small-scale neural in-loop filtering, there is difficulty when deploying them in large-scale models since both schemes require explicit parameter signaling within the bitstream, leading to prohibitive overhead. Existing parameter-generation networks avoid these transmission costs but introduce an excessive number of parameters. To address this, we propose modeling convolutional parameters as a linear combination of pre-trained kernels, where weights are adaptively estimated via input-driven attention. Specifically, we propose a multi-scale parameter-adaptive convolution and its extension, with side information, enabling parameter adaptation without transmission overhead or significant computational costs. Furthermore, we have designed an efficient parameter adaptive in-loop filtering network with the proposed convolutions to balance parameter efficiency and reconstruction performance. To improve the distortion guidance provided by side information, we have incorporated gradient information. Experiments on VTM-11.0 demonstrate {7.89%, 18.25%, and 19.15%} bitrate savings for {Y, U, and V} components, outperforming fixed-parameter baselines by an average of 1.41% with negligible computational overhead.