CamWorldQA introduces the first benchmark for assessing the perceptual quality of camera‑controlled world video generation, featuring 720 videos generated by six methods from 20 source videos across six camera trajectories, each scored by human raters. The paper also presents CWQA, a no‑reference quality assessment network that combines spatial, temporal motion, and optical flow features to predict quality scores. Experiments show CWQA outperforms existing VQA methods on the CamWorldQA dataset.
Scalable Neural Video Representation Compression (S-NVRC) introduces a scalable implicit neural representation (INR) video codec that supports fine-grained bitrate and decoding‑complexity scalability from a single embedded bitstream. It uses a coarse‑to‑fine prefix for feature grids and a nested prefix for network layers, enabling a wide range of operating points while maintaining a single encoding. On the UVG dataset, S‑NVRC outperforms SHM 12.4 and multi‑layer VTM‑20.0 by 43.7 % and 5.6 % in BD‑rate, respectively, and offers flexible complexity scalability.
By Tianhao Peng, Ho Man Kwan, Fan Zhang, Shan Liu, David Bull
arXiv:2608.21495v1 Announce Type: cross
Abstract: The latest video coding standard, H.266/VVC, has demonstrated significant improvements in compression efficiency compared to H.265/HEVC. Despite its...
By Sang NguyenQuang, Hieu Bui Minh, Dang BuiDinh, Xiem HoangVan
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.
By Yichong Xia, Qinhong Wu, Qinhong Wu, Jinpeng Wang, Zeyuan Chen, Haoqian Wang
arXiv:2512.07480v2 Announce Type: replace
Abstract: While traditional and neural video codecs (NVCs) have achieved remarkable rate-distortion performance, improving perceptual quality at low bitrates...
By Naifu Xue, Zhaoyang Jia, Jiahao Li, Bin Li, Zihan Zheng, Yuan Zhang, Yan Lu
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.
By Tamer Shanableh
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
arXiv:2606. 28027v1 Announce Type: cross Abstract: Neural video codecs have surpassed classical codecs in coding efficiency but remain impractical for deployment due to cross-platform incompatibility and high computational cost.
By Tanel P\"arnamaa, Martin Lumiste, Ardi Loot, Evgenii Indenbom, Andrei Znobishchev, Ando Saabas
arXiv:2606. 05861v1 Announce Type: cross Abstract: The rapid development of large language models(LLMs) has led to remarkable advances in natural language processing.
By Rui Wang, Yan Zhao, Li Song, Zhengxue Cheng
arXiv:2607. 01086v1 Announce Type: cross Abstract: The evaluation of long-term video quality understanding remains an open challenge for large vision-language models (LVLMs).
By Arpita Nema, Hanwei Zhu, Xi Zhang, Weisi Lin
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.
By Hakan Emre Gedik, Shashank Gupta, Alan Bovik
The paper introduces MIRC, an overfitted image codec that quantizes all components—including latents, synthesis network, and entropy models—within a single rate‑distortion objective, following the neural video representation codec NVRC. It adds a multi‑scale representation with cross‑stage parameter sharing to capture cross‑scale redundancy, achieving a 10.5% BD‑rate saving over VVC on the CLIC2020 professional validation set. MIRC also offers configurable decoding complexity ranging from 1.2 to 2.9 kMAC per pixel, allowing deployment to match specific resource budgets.