Video Coding and Compression Technologies

Latest papers

• Implicit Neural Representation for Hyperspectral Video Compression

  Alfredo Scalera, Paul Murray, Jaime Zabalza · 28 September 2026

  With the advent of snapshot cameras, hyperspectral video is becoming more readily available. In recent years, new applications have emerged which have led to increasingly larger datasets. However, hyperspectral video compression remains in the early stages. In this study, we explore the use of impli…

• ScoutNeRV: Rapid Encoding of Grid-Based Video INRs via ScoutNet

  Naser Alizada, Farhang Baghban, Hashem Pishkar, Ali Mousavi · 24 September 2026

  Implicit neural representations (INRs) have emerged as a promising paradigm for video compression, providing compact neural representations with flexible spatial and temporal reconstruction. Hierarchical grid-based architectures such as HiNeRV achieve strong rate--distortion performance, but require…

• Information Capacity of Generative Video Compression: Quantifying the Rate-Compute Exchange at Identical Quality

  Cheng Yuan, Jiawei Shao, Xuelong Li · 24 September 2026

  Under the AI Flow framework, communication networks distribute intelligence across devices, edge servers, and clouds, and computation at the receiver becomes a resource that can substitute for transmitted bits. Generative video compression (GVC) embodies this exchange by sending compact tokens with …

• Flexible-Region Based Adaptive In-Loop Filter for Video Coding

  Xuewei Meng, Chuanmin Jia, Jing Cui, Shanshe Wang, Siwei Ma · 22 September 2026

  Adaptive loop filter (ALF) for video coding, which is designed to minimize the mean square error between original and reconstructed samples by using Wiener-based filter, has attracted increasing attention for its significant capability in improving coding efficiency. In the second and third Audio Vi…

• Resolution-Flexible Decoding for Hybrid Neural Video Representations

  Taiga Hayami, Masaya Takabe, Hiroshi Watanabe · 22 September 2026

  Neural video representations (NVRs) represent videos using neural network parameters and, in hybrid formulations, frame-wise latent embeddings. Although hybrid NVRs can improve reconstruction quality by using content-adaptive latent embeddings, their latent spatial sizes and decoder upsampling sched…

• Perceptual Refinement of an End-to-End Video Streaming Pipeline via Generative AI Layers

  Emanuele Artioli, Farzad Tashtarian, Christian Timmerer · 18 September 2026

  Traditional codecs treat every region of a frame alike; a generative layer can instead degrade the regions a viewer attends to least and reconstruct them at the client. We present PRESLEY, which extends the prior conference work ELVIS by replacing destructive block removal with adaptive in-place deg…

• GenStream: Semantic Streaming Framework for Generative Reconstruction of Human-centric Media

  Emanuele Artioli, Daniele Lorenzi, Shivi Vats, Farzad Tashtarian, Christian Timmerer · 17 September 2026

  Video streaming dominates global internet traffic, yet conventional pipelines remain inefficient for structured, human-centric content such as sports, performance, or interactive media. Standard codecs re-encode entire frames, foreground and background alike, treating all pixels uniformly and ignori…

• Semantic-Aware Neural Video Codec for Error-Resilient Low-Latency Transmission

  Matin Mortaheb, Homa Esfahanizadeh, Jinfeng Du, Harish Viswanathan · 16 September 2026

  Emerging physical AI systems require low-latency, task-oriented video communication over unreliable channels. We propose a semantic-aware multi-level neural video coding method for robust low-latency video transmission over unreliable channels that are abstracted as multi-level packet erasure channe…

• tcnerv:dual-domain temporal context modeling for implicit neural video compression

  Xuezhi Xiang, Yixin Zhao, Heqi Xiang, Jiayao Liu, Shanjun Zhang · 16 September 2026

  Video compression aims to minimize reconstruction distor tion under a constrained bit rate. Existing video implicit neural representations (INRs) often decode frames independently, leaving intermediate features unconditioned on previous reconstructions and content embeddings without explicit tempora…

• Projection-Aware End-to-End Learned Video Compression for 360-Degree Video

  Niloofar Maani · 7 September 2026

  360-degree video supports immersive applications such as virtual reality, autonomous driving, and education. Because spherical content cannot be processed directly by conventional video codecs, it must first be mapped to a two-dimensional projection. Projection choice affects spatial continuity, sam…

• Fractional-Order Adaptive Motion Magnification: Phase-Reliability Weighting for Noise-Constrained Video Amplification

  Alejandro Garnung Men\'endez · 7 September 2026

  Eulerian video amplification boosts sub-pixel motion by band-pass filtering per-pixel intensity traces and applying a uniform gain. That gain ignores local structure, so sensor noise is amplified together with the signal, especially in textureless regions where the monogenic phase is unreliable. We …

• Scalable Neural Video Representation Compression

  Tianhao Peng, Ho Man Kwan, Fan Zhang, Shan Liu, David Bull · 7 September 2026

  Scalable video coding (SVC) encodes a video into a layered bitstream consisting of a base layer and one or multiple enhancement layers, enabling decoding at different bitrate/quality/resolution operating points to accommodate diverse device capabilities and network conditions. Due to its practical f…

• Neural Video Compression Based on Deformable Temporal Alignment and Difference-aware Fusion

  Chuyue Shan, Songlin Sun, Wang Chenwei, Shen Zihan · 4 September 2026

  In conditional coding-based neural video compression, the quality of temporal context directly affects compression per- formance. Existing methods mostly construct context from prop- agated reference features, but they are vulnerable to motion esti- mation and local alignment errors in regions with …

• VoRTeC: Taming Foundation Flow for One-step Real time Video Compression

  Yichong Xia, Qinhong Wu, Qinhong Wu, Jinpeng Wang, Zeyuan Chen, Haoqian Wang · 3 September 2026

  Ultra-low bitrate video compression still faces critical challenges: traditional neural video compression inevitably introduces blurring artifacts, while diffusion-based generative video compression suffers from excessive decoding latency and poor temporal consistency. To address these issues, we pr…

• Can LLMs Design Video Coding Tools? A Case Study on Planar Mode

  Yingwen Zhang, Meng Wang, Liqiang He, Shiqi Wang · 2 September 2026

  This paper explores whether large language models (LLMs) can design video coding tools, a highly challenging task due to the intricate algorithmic coupling of tool modifications. In particular, we present an empirical case study on the Planar mode, a long-standing intra prediction tool in video codi…

• S$^2$GS: Structured Sparse Gaussian Streaming for Efficient Free-Viewpoint Video Reconstruction on Edge-IoT Devices

  Yiwei Li, Jiannong Cao, Weixun Gao, Rui Cao, Songye Zhu, Yinfeng Cao, Mingjin Zhang · 21 August 2026

  Streaming reconstruction of Free-Viewpoint Videos (FVVs) supports immersive Internet of Things (IoT) services, such as telepresence and digital twin visualization. Existing methods suffer from high per-frame optimization time and large storage footprints, limiting deployment on resource-constrained …

• QuARC-GS: Quantized Anchored Residual Coding for Compact Dynamic Scene Streaming with Gaussian Splatting

  Vu Trung Nghia Nguyen, Yuchen Wang, Kyung Chul Lee, Kevin C. Zhou · 20 August 2026

  3D scene representation techniques such as neural radiance fields (NeRFs) and Gaussian splatting have made substantial progress in novel view synthesis, achieving high-quality renderings from arbitrary view angles. More recently, such techniques have been extended to dynamic 3D scenes; however, achi…

• Context-Matched Distillation: Teacher Causality for Autoregressive Video Distillation

  Hmrishav Bandyopadhyay, Xuanchi Ren, Zijian Huang, Jay Zhangjie Wu, Tianshi Cao, Ruilong Li, Bryan Chu, Sanja Fidler, Yi-Zhe Song, Zian Wang · 14 August 2026

  Interactive autoregressive video generation demands both low-latency rollouts and precise online control. Few-step distillation accelerates generation by reducing denoising steps, while online control imposes a causal constraint: frames and blocks should depend on history and controls available duri…

• Think in Sets for Streaming Video Token Compression

  Moxu Duan, Jingwen Fu, Yuwang Wang · 4 August 2026

  Streaming VideoLLMs process frames causally while visual tokens grow continuously, making compression essential for controlling prefilling latency and memory. Existing training-free methods independently rank tokens, ignoring marginal-gain interactions among retained tokens. We argue that streaming …

• ReGenVC: End-to-End Real-Time Generative Video Coding at Ultra-Low Bitrate

  Zheyuan Zhang, Johnson Wu · 31 July 2026

  We present ReGenVC, an end-to-end generative video codec that compresses talking-head video to an ultra-low bitrate and decodes it in real time. The encoder reduces a source clip to a compact bitstream -- a neurally compressed first frame, per-frame pose keypoints, and metadata -- totaling about 26 …

• ENCORE: Event-Assisted Complementary Motion Refinement for Learned Video Compression

  Shuhan Ye, Hongbin Yu, Chenqi Kong, Pingchuan Ma, Chong Wang, Jun Wan, Qixin Zhang · 31 July 2026

  Learned video compression relies on accurate temporal modeling to remove redundancy between adjacent frames. However, most existing codecs infer motion solely from discretely sampled RGB frames, making their estimates vulnerable to fast motion, blur, occlusion, weak texture, low illumination, and ab…

• ScalablePromptus: Scalable and High-Fidelity Prompt-Based Video Streaming

  Zehao Cao, Bowei Xu, Xun Cao, Zhan Ma, Hao Chen · 29 July 2026

  Prompt-based video streaming transmits compact semantic prompts instead of pixel-level content for generative reconstruction, enabling ultra-low-bitrate communication. However, the state-of-the-art Promptus framework is vulnerable to network fluctuation, where partially received prompts lead to cata…

• SANA-Video 2.0: Hybrid Linear Attention with Attention Residuals for Efficient Video Generation

  Junsong Chen, Jincheng Yu, Yitong Li, Shuchen Xue, Haozhe Liu, Jingyu Xin, Yuyang Zhao, Tian Ye, Zhangjie Wu, Zian Wang, Daquan Zhou, Ping Luo, Song Han, Enze Xie · 24 July 2026

  We introduce SANA-Video 2.0, a hybrid video diffusion transformer instantiated at 5B and 14B scales under a unified architecture. Designed to generate high-quality video up to 720p on a single GPU, SANA-Video 2.0 matches full-softmax video DiTs in quality while retaining the favorable long-sequence …

• Generative Transmission: Rethinking Computation, Bandwidth, and Memory in Communication

  Xiangyu Chen, Jixiang Luo, Yuankai Fan, Haibin Huang, Chi Zhang, Xuelong Li · 21 July 2026

  Under the AI Flow framework, communication is shifting from transmitting fidelity-oriented information flows toward delivering task-oriented and perception-oriented token flows across heterogeneous network resources. Video communication is a fundamental component of modern information networks. Howe…

• FVAttn: Adaptive Sparse Attention with Runtime Load Balancing for Video Generation

  Hao Liu, Chenghuan Huang, Ye Huang, Zhiying Wen, Hao Liu, Mohan Zhang, Chen Li, Ziyang Ma, Jing Lyu, Jiangsu Du · 20 July 2026

  Video Diffusion Transformers process long spatio-temporal sequences, making self-attention the main bottleneck in high-resolution video generation. Training-free sparse attention reduces this cost, but adaptive Top-$p$ routing creates uneven per-head workloads under multi-GPU sequence parallelism. T…