Most neural video codecs rely on temporal conditioning, which makes them susceptible to error propagation over long sequences. While Transformer-based architectures like the VCT offer a drift-free alternative, they suffer from high computational complexity and inferior RD performance. The recent SWA addresses these shortcomings by reducing complexity and enhancing RD performance, yet it restricts decoding to a strictly sequential raster-scan order, creating a critical bottleneck in decoding latency. To resolve this, we propose P-SWA, utilizing diagonal wavefronts to enable parallel decoding. By embedding a hyperprior and introducing an accumulator to fuse side information and local spatial context, our method increases decoding speed by 36% over the parallel VCT. Simultaneously, it achieves Bj{\o}ntegaard Delta-rate savings of up to 10.0% for I-frames and 7.1% for P-frames over the SWA baseline.
@article{arxiv.2605.20977,
title = {Parallel Context Modeling for Sliding Window Attention in Neural Video Coding},
author = {Alexander Kopte and André Kaup},
journal= {arXiv preprint arXiv:2605.20977},
year = {2026}
}