Video Compression

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3 papers in the last four weeks, level with the four weeks before. 0.0% of all new papers.

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A Projection-Based Surrogate Gradient Interpretation for Neural Codec Wrappers
Jun 12, 2026Esteban Pesnel, Julien Le Tanou, Michael Ropert +2Neural Network Optimization Neural Video Compression
COMPACT · MediaKind, Rennes, France · INRIA, Rennes, France
High-Fidelity Video Compression based on Invertible Neural Transform and Implicit Conditioning
Jun 11, 2026Siyue Teng, Ho Man Kwan, Yuxuan Jiang +2Invertible Neural Networks Neural Video Compression
Visual Information Lab, University of Bristol, UK
Ultra-Fast Neural Video Compression
Jun 3, 2026Jiahao Li, Wenxuan Xie, Zhaoyang Jia +4Neural Video Compression Video Compression
Microsoft Research Asia · University of Science and Technology of China · *Done during Zhaoyang Jia’s internship at Microsoft Research Asia.
Spatial Artifact Coherence Determines Codec Robustness in Patch-Based rPPG
Jun 2, 2026Achraf Ben AhmedRemote Photoplethysmography Heart Rate Estimation
PlesmoSense SARL
Spatio-Temporal Correlation Guided Geometric Partitioning for Versatile Video Coding
Jun 1, 2026Xuewei Meng, Chuanmin Jia, Xinfeng Zhang +2Video Compression Entropy Coding
Institute of Digital Media, Department of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China · School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100190, China · Information Technology R&D Innovation Center of Peking University, Shaoxing 312000, China +1
Deformable Wiener Filter for Future Video Coding
Jun 1, 2026Xuewei Meng, Chuanmin Jia, Xinfeng Zhang +2Image Enhancement Video Compression
National Engineering Research Center of Visual Technology, School of Computer Science, Peking University, Beijing 100871, China · Core Media Technology, Disney Streaming · Wangxuan Institute of Computer Technology, Peking University, Beijing 100871, China +3
NeR-SC: Adapting Neural Video Representation to Screen Content
May 26, 2026Ruohan Shi, Jiaoyan Zhao, Haogang FengImplicit Neural Representations Neural Video Compression
Management school The University of Sheffield · Undergraduate School of Artificial Intelligence Shenzhen Polytechnic University · School of Artificial Intelligence Shenzhen University of Information Technology
FAST-ME: Foundation-aware Adaptive Stopping for Motion Estimation for Efficient IoT Video Analysis
May 22, 2026Kakia Panagidi, Stathes HadjieftymiadisSequential Decision Making Optimal Stopping
Department of Informatics & Telecommunications National and Kapodistrian University of Athens Athens, Greece
CATRF: Codec-Adaptive TriPlane Radiance Fields for Volumetric Content Delivery
May 18, 2026Tung-I Chen, Lingdong Wang, Subhransu Maji +1Rate-Distortion Optimization 3D Scene Representation
University of Massachusetts Amherst
TVRN: Invertible Neural Networks for Compression-Aware Temporal Video Rescaling
May 15, 2026Xinmin Feng, Li Li, Dong Liu +1Invertible Neural Networks Video Compression
MOE Key Laboratory of Brain-Inspired Intelligent Perception and Cognition, University of Science and Technology of China, Hefei 230093, China
Neural Video Compression with Domain Transfer
May 13, 2026Tiange Zhang, Rongqun Lin, Xiandong Meng +4Test-Time Adaptation Cross-Domain Transfer Learning
Shenzhen Graduate School, Peking University, Shenzhen, China · Pengcheng Laboratory, Shenzhen, China · School of Computer Science, Peking University, Beijing, China
ChronoSC: Task-Oriented Semantic Communication via Temporal-to-Color Encoding
May 11, 2026Phuc H. Nguyen, Trung T. Nguyen, Quy N. Duong +1Semantic Communication Video QA
Smart Green Transformation Center (GREEN-X), VinUniversity, Vietnam. · School of Computer Science and Statistics, Trinity College Dublin, Ireland
Kelvin v1.0: A Neural Pre-Encoder for H.264: A standards-compliant learned preprocessor with -27.62% BD-VMAF on UVG
May 10, 2026Marco GrazianoLearned Image Compression Video Compression
Graziano Labs Corp., Palo Alto, CA, USA.
CFE-PPAR: Compression-friendly encryption for privacy-preserving action recognition leveraging video transformers
May 7, 2026Haiwei Lin, Shoko Imaizumi, Hitoshi KiyaVideo Action Recognition Privacy-Preserving ML
Graduate School of Informatics Chiba University Chiba, Japan · Faculty of System Design Tokyo Metropolitan University Tokyo, Japan
Active Sampling for Ultra-Low-Bit-Rate Video Compression via Conditional Controlled Diffusion
May 4, 2026Amirhosein Javadi, Shirin Saeedi Bidokhti, Tara JavidiLearned Image Compression Video Diffusion Models
Department of Electrical and Computer Engineering University of California San Diego · Department of Electrical and Systems Engineering University of Pennsylvania · Department of Electrical and Computer Engineering2026 University of California San Diego
Image Compression with Bubble-Aware Frame Rate Adaptation for Energy-Efficient Video Capsule Endoscopy
Apr 28, 2026Oliver Bause, Jörg Gamerdinger, Julia Werner +1Endoscopy Medical Imaging
University of Tübingen, Faculty of Science, Department of Computer Science, Embedded Systems Group
FDIM: A Feature-distance-based Generic Video Quality Metric for Versatile Codecs
Apr 27, 2026Jiayi Wang, Lichun Zhang, Xiaoqi Zhuang +3Video Quality Assessment Video Compression
College of Information Science and Electronic Engineering, Zhejiang University, and also with the Zhejiang Key Laboratory of Multimodal Communication Networks and Intelligent Information Processing, Hangzhou 310058, China · Central Media Technology Institute, Huawei, Hangzhou 310058, China
NeuralLVC: Neural Lossless Video Compression via Masked Diffusion with Temporal Conditioning
Apr 3, 2026Tiberio Uricchio, Marco BertiniMasked Diffusion Models Neural Video Compression
University of Pisa · Pisa, Italy · Università degli Studi di Firenze +1
LRConv-NeRV: Low Rank Convolution for Efficient Neural Video Compression
Mar 18, 2026Tamer ShanablehVideo Representation Learning Neural Video Compression
American University of Sharjah, UAE, Department of Computer Science and Engineering
Single-step Diffusion-based Video Coding with Semantic-Temporal Guidance
Dec 8, 2025Naifu Xue, Zhaoyang Jia, Jiahao Li +4Diffusion Model Guidance Video Diffusion Models
Communication University of China · University of Science and Technology of China · Microsoft Research Asia
Blind Quality Enhancement of Compressed Video via Fine-Grained Degradation-Guided Sequential Inference
Nov 20, 2025Li Yu, Yingbo Zhao, Shiyu Wu +3Adaptive Inference Representation Learning
School of Computer Science, Nanjing University of Information Science & Technology, Nanjing, China, and also with Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing, China · School of Computer Science, Nanjing University of Information Science & Technology, Nanjing, China · School of Software, Nanjing University of Information Science & Technology, Nanjing, China +3
Real-Time Neural Video Compression with Unified Intra and Inter Coding
Oct 16, 2025Hui Xiang, Yifan Bian, Li Li +3Learned Image Compression Neural Video Compression
University of Science and Technology of China · Tencent Shannon Lab

Jun 12, 2026, cs.CVJ/K move · Enter open · S save

A Projection-Based Surrogate Gradient Interpretation for Neural Codec Wrappers

Esteban Pesnel, Julien Le Tanou, Michael Ropert, Aline Roumy+1

COMPACT · MediaKind, Rennes, France · INRIA, Rennes, France

Neural Network Optimization Neural Video Compression Video Compression

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Neural wrappers are learned pre-and postprocessing networks designed to enhance the performance of conventional video codecs. Although these approaches can significantly improve compression efficiency, training them remains challenging due to the non-differentiability of video codecs, which arises from the multiple discrete decisions involved in the encoding process. Surrogate gradients have recently emerged as an effective solution for enabling end-to-end learning with conventional codecs. They offer two main advantages: they avoid training an additional network to mimic the codec, and they can improve compression performance. In particular, the recently proposed SCALED method, which leverages the true compression error, has shown strong results for training neural pre-processors such as downscalers. However, this SCALED gradient was originally introduced as a reparameterization trick, which limits its interpretability. In this paper, we show that this surrogate gradient can be interpreted as a first-order local approximation of the video codec, providing insight into its effectiveness. We further demonstrate that it is effective not only for learning downscaling operations, but also for the more challenging task of full neural wrapping with pre-and post-processing networks. Finally, we show that the approach generalizes well across different video codecs, quality factors, and tasks, including multiple downscaling ratios, yielding BD-Rate (PSNR) reductions of up to -23.59% on x264 and -20.07% on VVenC relative to standard resampling baselines.

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