The paper proposes a Flexible-Region Based Adaptive In-Loop Filter (FRALF) for video coding, extending the existing Adaptive Loop Filter (ALF) used in AVS3 by introducing multiple region partition templates such as 2×4, 4×4, 4×8, and 8×8. It also presents a filter coefficient merging algorithm that estimates distortion levels across different partition regions to further improve coding efficiency. Experimental results demonstrate that FRALF outperforms the current region-based ALF in AVS3 while keeping complexity increases relatively low.
By Xuewei Meng, Chuanmin Jia, Jing Cui, Shanshe Wang, Siwei Ma
arXiv:2607.12186v2 Announce Type: replace
Abstract: In-loop filters have been comprehensively explored during the development of video coding standards due to their remarkable noise-reduction capabil...
By Zhaoyu Li, Xuewei Meng, Jiaqi Zhang, Cheng Huang, Chuanmin Jia, Siwei Ma, Yun Jiang
In-loop filters have been comprehensively explored during the development of video coding standards due to their remarkable noise-reduction capability. In the early stage of video coding, in-loop filters, such as Deblocking Filter, Sample Adaptive Offset, and Adaptive Loop Filter, were performed separately for each component.
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
The paper introduces MIRC, an overfitted image codec that quantizes and entropy‑codes all components—including latents, synthesis network, and entropy models—within a single end‑to‑end rate‑distortion framework inspired by NVRC. It adds a multi‑scale representation with cross‑stage parameter sharing to capture cross‑scale redundancy, yielding a 10.5 % BD‑rate saving over VVC on the CLIC2020 professional set. MIRC offers multiple configurations ranging from 1.2 to 2.9 kMAC per pixel, allowing decoding complexity to be tuned to deployment needs.
By Tianhao Peng, Ho Man Kwan, Fan Zhang, Shan Liu, David Bull
arXiv:2611.05359v2 Announce Type: replace
Abstract: This paper presents GSCV++, a lightweight pipeline for 3D {G}aussian {S}platting (GS) sequences {C}ompression with standard {V}ideo codecs. To meet...
By Qi Yang, Yingzhan Xu, Kai Zhang, Li Zhang
The paper investigates whether large language models (LLMs) can design video coding tools, focusing on the Planar mode used in video coding standards. Using a generation-and-evaluation loop, the LLM generates new Planar predictors, which are then tested in the Fraunhofer Versatile Video Encoder (VVenC) and the Enhanced Compression Model (ECM). Results show that the LLM-generated mode can outperform the conventional Planar mode, achieving a 0.18% bitrate saving with a 0.4% complexity increase, and that similar gains are possible when integrating the new predictor into ECM under low‑resolution settings.
By Yingwen Zhang, Meng Wang, Liqiang He, Shiqi Wang
arXiv:2604.06036v4 Announce Type: replace-cross
Abstract: Continuous inference over concurrent video streams imposes substantial compute and memory demands on vision-language model (VLM) serving. Str...
By Yulin Zou, Wenyan Chen, Yan Chen, Anya Rajan, JooYoung Park, Shivaraman Nitin, Luo Tao, Francisco Romero, Dmitrii Ustiugov
arXiv:2610.07795v1 Announce Type: new
Abstract: This paper presents a novel effective Gaussian Splatting (GS) sequence Compression method that utilizes the Video codec (GSCV). Existing video-based GS...
By Qi Yang, Shuting Xia, Le Yang, Geert Van Der Auwera, Zhu Li
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
This paper presents GSCV++, a lightweight pipeline for 3D {G}aussian {S}platting (GS) sequences {C}ompression with standard {V}ideo codecs. To meet the requirements of low-complexity and hardware comp...
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.