The paper introduces a neural video compression technique that improves temporal context quality by combining deformable temporal alignment with difference‑aware spatial selective fusion. A Context‑aware Temporal Alignment Module generates complementary temporal context, while a Difference‑aware Spatial Selective Fusion module adaptively selects reliable temporal information and suppresses misalignment. Experiments demonstrate that this approach yields better rate‑distortion performance compared to DCVC‑DC.
arXiv:2610.34765v1 Announce Type: new
Abstract: Multi-modal video fusion aims to generate temporally coherent videos with salient targets and rich structural details by integrating complementary info...
By Jingxue Huang, Xiaosong Li, Yi Lin
The paper introduces Neural Image Compression with Internal-External Prior Fusion and Alignment (IE-PFA), a context model that combines internal priors from the current image with an external dictionary shared by encoder and decoder. It employs a RWKV-based Spatial-Channel Prior Fusion (RSCPF) module for efficient fusion and a Self-Distilled Prior Alignment (SDPA) mechanism for alignment during training. The model supports variable-rate coding across 64 quality levels, achieves a 21.65% BD-Rate saving over VTM-22.0-Intra, uses only 29.9M parameters, and offers fast encoding (61 ms) and decoding (52 ms) speeds.
By Tiancheng Si, Wenlong Xu, Dahua Gao
TCNeRV is a new implicit neural video compression method that models temporal context in both feature and embedding domains. Its multi‑scale temporal‑context fusion module injects gated historical features across decoder scales, while temporal embedding‑residual coding predicts and encodes only the residual of each content embedding. With about 3 million parameters, TCNeRV achieves an average PSNR of 36.08 dB on the UVG dataset, outperforming HNeRV‑Boost by 2.20 dB and reducing BD‑rate by 22.06%, 66.73%, and 29.85% relative to HM, DCVC, and HiNeRV respectively.
By Xuezhi Xiang, Yixin Zhao, Heqi Xiang, Jiayao Liu, Shanjun Zhang
The paper introduces Event-guided Neural Video Codec (ENVC), a neural video compression method that incorporates event streams—capturing brightness changes between frames—into both motion and frame coding stages. By using event-guided motion priors and event-conditioned predictors, ENVC improves RGB compression efficiency, achieving significant BD-rate savings across six benchmarks. The authors also synthesize paired RGB-event data for training and demonstrate that the gains persist on large-motion sequences, highlighting events as a valuable complementary modality for video coding.
By Jiyun Kong, Jungwoo Kim, Enes Eray Demirtas, Touradj Ebrahimi, Jong-Seok Lee
In video understanding, vision-language models (VLMs) must ingest massive numbers of visual tokens, causing the computational and memory cost of the prefill stage to rise sharply. Such visual sequences are highly redundant along the spatio-temporal dimension, yet a high compression ratio is often accompanied by the loss of critical details.