Neural Video Compression has emerged in recent years, with condition-based frameworks outperforming traditional codecs. However, most existing methods rely solely on the previous frame's features to predict temporal context, leading to two critical issues. First, the short reference window misses long-term dependencies and fine texture details. Second, propagating only feature-level information accumulates errors over frames, causing prediction inaccuracies and loss of subtle textures. To address these, we propose the Long-term Spatio-Temporal Enhanced Context (L-STEC) method. We first extend the reference chain with LSTM to capture long-term dependencies. We then incorporate warped spatial context from the pixel domain, fusing spatio-temporal information through a multi-receptive field network to better preserve reference details. Experimental results show that L-STEC significantly improves compression by enriching contextual information, achieving 37.01% bitrate savings in PSNR and 31.65% in MS-SSIM compared to DCVC-TCM, outperforming both VTM-17.0 and DCVC-FM and establishing new state-of-the-art performance.
@article{arxiv.2512.12790,
title = {L-STEC: Learned Video Compression with Long-term Spatio-Temporal Enhanced Context},
author = {Tiange Zhang and Zhimeng Huang and Xiandong Meng and Kai Zhang and Zhipin Deng and Siwei Ma},
journal= {arXiv preprint arXiv:2512.12790},
year = {2025}
}
Comments
Accepted to Data Compression Conference (DCC) 2026 as an oral paper