arXiv:2609.39894v1 Announce Type: new Abstract: Different from natural videos, Screen Content Videos (SCVs) are characterized by abrupt motion, scene switches, and high-frequency details such as text...
High-Fidelity Video Quality Assessment (HFVQA) is a new framework that uses fixed-size spatio‑temporal patches across multiple scales, including the original resolution, to preserve low‑level quality cues and semantic context. It incorporates a lightweight auxiliary network that learns VQA‑specific saliency directly from quality supervision, enabling the model to focus on the most important spatio‑temporal regions. By combining high‑fidelity cues with task‑specific saliency, HFVQA achieves state‑of‑the‑art performance on standard no‑reference VQA benchmarks while processing only about 12% of the candidate patches, making it computationally efficient.
Video large language models (VLLMs) need long visual token sequences, making token compression essential for efficient inference. This paper introduces a training‑free token compression method that leverages existing video codec information—such as reference‑frame dependencies, motion vectors, and residuals—to guide low‑cost search for token protection and temporal merging. Experiments on three VLLMs and four benchmarks show that the method matches or surpasses the FlashVID baseline while dramatically reducing feature comparisons.
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.
arXiv:2608.28429v1 Announce Type: new Abstract: Event cameras generate asynchronous, sparse data streams with microsecond temporal resolution, but in moderate-to-high motion scenes they can produce a...
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.
The paper introduces VoRTeC, a video compression framework that leverages a foundational flow model to encode latent video representations compactly and predict their positions along flow trajectories. By integrating multi‑scale priors and avoiding access to flow‑matching network parameters, VoRTeC achieves one‑step decoding with high perceptual fidelity, while maintaining temporal consistency through tail‑frame reuse and prior caching. Experiments show a 58% reduction in bit consumption compared to prior diffusion‑based methods and a decoding speed increase ranging from 3 to 197 times, reaching 13 FPS at 720p and 32 FPS at 480p.
LRConv-NeRV introduces low‑rank separable convolutions into the NeRV neural video decoder, replacing selected dense 3x3 layers to reduce computational load and memory usage. By applying low‑rank factorization progressively from the largest to earlier decoder stages, the method offers controllable trade‑offs between reconstruction quality and efficiency. Experiments show that applying LRConv only to the final decoder stage cuts decoder complexity by 68% and model size by 9.3% with negligible quality loss, while INT8 quantization preserves performance close to the dense baseline.
Video large language models (VLLMs) need long visual token sequences, making token compression essential for efficient inference. This paper introduces a training‑free token compression technique that leverages existing video codec information—such as reference‑frame dependencies, motion vectors, and residuals—to guide low‑cost search priors. By aligning these codec cues with the visual token grid, the method creates compact candidate pools for token protection and localized candidates for temporal token merging, reducing feature comparisons while maintaining performance comparable to or better than the FlashVID baseline across multiple VLLMs and benchmarks.
arXiv:2608.29331v1 Announce Type: new Abstract: Video traffic constitutes a significant share of global web traffic. To reduce its volume, video codecs have been developed and continuously improved....
The paper introduces UniGVC, a unified generative video codec that supports both low‑delay streaming and high‑quality playback from a single bitstream. It offers two decoding modes: UniGVC‑BI, a bidirectional diffusion model with dense attention for quality‑oriented random‑access decoding, and UniGVC‑AR, a windowed autoregressive diffusion model for low‑delay streaming. Both modes share a hierarchical latent codec that produces ultra‑low‑bitrate conditions, enabling the BI model to act as a teacher for distilling the AR model, while a lightweight conditional decoder further reduces streaming latency.
arXiv:2609.37925v1 Announce Type: cross Abstract: Autoregressive (AR) video diffusion enables low-latency, streamable video generation, but prediction errors often accumulate over long rollouts. Trai...