Adaptive Neural In-Loop Filtering via Boundary-Aware Skipping and Time-Distortion Optimization

Neural network-based in-loop filters (NNILFs) have recently been integrated into emerging video coding frameworks to enhance reconstruction quality beyond the capabilities of traditional signal processing filters. However, the high computational complexity of these filters significantly increases encoding and decoding time, hindering their real-time deployment. In this paper, we propose an efficient neural in-loop filter control framework that improves decoding efficiency while preserving coding performance. Our approach introduces boundary strength-aware control to selectively skip NNILF operations on visually less critical regions. In addition, a time-distortion optimization (TDO) strategy is presented to adaptively manage the trade-off between visual distortion and inference time during encoding. Experimental results on the NNVC reference software show that the proposed methods reduce decoding time by up to 23% and 21% for the Random Access (RA) and Low Delay B (LDB) configurations, respectively. Apart from a minimal luma loss only in RA, chroma gains were also achieved, with Y: 0.10% U: -0.01% V: -0.14% for RA, and Y: 0.00% U: -1.34% V: -0.94% for LDB. These results show that the proposed control framework offers a practical and effective solution for integrating NN-based tools into future video coding systems.