Resi-VidTok: An Efficient and Decomposed Progressive Tokenization Framework for Ultra-Low-Rate and Lightweight Video Transmission

• URL: http://arxiv.org/abs/2510.25002v1
• Date: Tue, 28 Oct 2025 22:02:36 GMT
• Title: Resi-VidTok: An Efficient and Decomposed Progressive Tokenization Framework for Ultra-Low-Rate and Lightweight Video Transmission
• Authors: Zhenyu Liu, Yi Ma, Rahim Tafazolli, Zhi Ding,
• Abstract summary: Resi-VidTok is a Resilient Tokenization-Enabled framework for ultra-low-rate and lightweight video transmission.<n>A key contribution is a resilient 1D tokenization pipeline for video that integrates differential temporal token coding.<n>Results indicate robust visual and semantic consistency at channel bandwidth ratios (CBR) as low as 0.0004 and real-time reconstruction at over 30 fps.
• Score: 35.3961976297755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-time transmission of video over wireless networks remains highly challenging, even with advanced deep models, particularly under severe channel conditions such as limited bandwidth and weak connectivity. In this paper, we propose Resi-VidTok, a Resilient Tokenization-Enabled framework designed for ultra-low-rate and lightweight video transmission that delivers strong robustness while preserving perceptual and semantic fidelity on commodity digital hardware. By reorganizing spatio--temporal content into a discrete, importance-ordered token stream composed of key tokens and refinement tokens, Resi-VidTok enables progressive encoding, prefix-decodable reconstruction, and graceful quality degradation under constrained channels. A key contribution is a resilient 1D tokenization pipeline for video that integrates differential temporal token coding, explicitly supporting reliable recovery from incomplete token sets using a single shared framewise decoder--without auxiliary temporal extractors or heavy generative models. Furthermore, stride-controlled frame sparsification combined with a lightweight decoder-side interpolator reduces transmission load while maintaining motion continuity. Finally, a channel-adaptive source--channel coding and modulation scheme dynamically allocates rate and protection according to token importance and channel condition, yielding stable quality across adverse SNRs. Evaluation results indicate robust visual and semantic consistency at channel bandwidth ratios (CBR) as low as 0.0004 and real-time reconstruction at over 30 fps, demonstrating the practicality of Resi-VidTok for energy-efficient, latency-sensitive, and reliability-critical wireless applications.

Related papers

• Cross-Attention Transformer for Joint Multi-Receiver Uplink Neural Decoding [0.0]
  We propose a cross-attention Transformer for joint decoding of OFDM signals.<n>A shared per-receiver encoder learns time-frequency structure within each received grid.<n>A token-wise cross-attention module fuses the receivers to produce soft log-likelihood ratios for a standard channel decoder.
  arXiv  Detail & Related papers  (2026-02-04T16:34:48Z)
• Single-step Diffusion-based Video Coding with Semantic-Temporal Guidance [24.88807532823577]
  We propose S2VC, a Single-Step diffusion based Video Codec that integrates a conditional coding framework with an efficient single-step diffusion generator.<n>We show that S2VC delivers state-of-the-art perceptual quality with an average 52.73% saving over prior perceptual methods.
  arXiv  Detail & Related papers  (2025-12-08T12:05:30Z)
• DiTVR: Zero-Shot Diffusion Transformer for Video Restoration [48.97196894658511]
  DiTVR is a zero shot video restoration framework that couples a diffusion transformer with trajectory aware attention and a flow consistent sampler.<n>Our attention mechanism aligns tokens along optical flow trajectories, with particular emphasis on vital layers that exhibit the highest sensitivity to temporal dynamics.<n>The flow guided sampler injects data consistency only into low-frequency bands, preserving high frequency priors while accelerating cache.
  arXiv  Detail & Related papers  (2025-08-11T09:54:45Z)
• SIEDD: Shared-Implicit Encoder with Discrete Decoders [36.705337163276255]
  Implicit Neural Representations (INRs) offer exceptional fidelity for video compression by learning per-video optimized functions.<n>Existing attempts to accelerate INR encoding often sacrifice reconstruction quality or crucial coordinate-level control.<n>We introduce SIEDD, a novel architecture that fundamentally accelerates INR encoding without these compromises.
  arXiv  Detail & Related papers  (2025-06-29T19:39:43Z)
• FCA2: Frame Compression-Aware Autoencoder for Modular and Fast Compressed Video Super-Resolution [68.77813885751308]
  State-of-the-art (SOTA) compressed video super-resolution (CVSR) models face persistent challenges, including prolonged inference time, complex training pipelines, and reliance on auxiliary information.<n>We propose an efficient and scalable solution inspired by the structural and statistical similarities between hyperspectral images (HSI) and video data.<n>Our approach introduces a compression-driven dimensionality reduction strategy that reduces computational complexity, accelerates inference, and enhances the extraction of temporal information across frames.
  arXiv  Detail & Related papers  (2025-06-13T07:59:52Z)
• FANeRV: Frequency Separation and Augmentation based Neural Representation for Video [32.35716293561769]
  We present a Frequency Separation and Augmentation based Neural Representation for video (FANeRV)<n>FANeRV explicitly separates input frames into high and low-frequency components using discrete wavelet transform.<n>A specially designed gated network effectively fuses these frequency components for optimal reconstruction.
  arXiv  Detail & Related papers  (2025-04-09T10:19:35Z)
• Rethinking Video Tokenization: A Conditioned Diffusion-based Approach [58.164354605550194]
  New tokenizer, Diffusion Conditioned-based Gene Tokenizer, replaces GAN-based decoder with conditional diffusion model.<n>We trained using only a basic MSE diffusion loss for reconstruction, along with KL term and LPIPS perceptual loss from scratch.<n>Even a scaled-down version of CDT (3$times inference speedup) still performs comparably with top baselines.
  arXiv  Detail & Related papers  (2025-03-05T17:59:19Z)
• BF-STVSR: B-Splines and Fourier-Best Friends for High Fidelity Spatial-Temporal Video Super-Resolution [14.082598088990352]
  We propose BF-STVSR, a C-STVSR framework with two key modules tailored to better represent spatial and temporal characteristics of video.<n>Our approach achieves state-of-the-art in various metrics, including PSNR and SSIM, showing enhanced spatial details and natural temporal consistency.
  arXiv  Detail & Related papers  (2025-01-19T13:29:41Z)
• Deep Learning-Based Image Compression for Wireless Communications: Impacts on Reliability,Throughput, and Latency [41.77014570882275]
  In wireless communications, efficient image transmission must balance reliability, throughput, and latency. We investigate two state-of-the-art learning-based models: the hyperprior model and Vector Quantized Generative Adversarial Network (VQGAN) We propose progressive versions of both models, enabling partial image transmission and decoding under imperfect channel conditions.
  arXiv  Detail & Related papers  (2024-11-16T01:14:55Z)
• Low-Light Video Enhancement via Spatial-Temporal Consistent Decomposition [52.89441679581216]
  Low-Light Video Enhancement (LLVE) seeks to restore dynamic or static scenes plagued by severe invisibility and noise.<n>We present an innovative video decomposition strategy that incorporates view-independent and view-dependent components.<n>Our framework consistently outperforms existing methods, establishing a new SOTA performance.
  arXiv  Detail & Related papers  (2024-05-24T15:56:40Z)
• Boosting Neural Representations for Videos with a Conditional Decoder [28.073607937396552]
  Implicit neural representations (INRs) have emerged as a promising approach for video storage and processing. This paper introduces a universal boosting framework for current implicit video representation approaches.
  arXiv  Detail & Related papers  (2024-02-28T08:32:19Z)
• Neural Residual Radiance Fields for Streamably Free-Viewpoint Videos [69.22032459870242]
  We present a novel technique, Residual Radiance Field or ReRF, as a highly compact neural representation to achieve real-time free-view rendering on long-duration dynamic scenes. We show such a strategy can handle large motions without sacrificing quality. Based on ReRF, we design a special FVV that achieves three orders of magnitudes compression rate and provides a companion ReRF player to support online streaming of long-duration FVVs of dynamic scenes.
  arXiv  Detail & Related papers  (2023-04-10T08:36:00Z)
• Structured Sparsity Learning for Efficient Video Super-Resolution [99.1632164448236]
  We develop a structured pruning scheme called Structured Sparsity Learning (SSL) according to the properties of video super-resolution (VSR) models. In SSL, we design pruning schemes for several key components in VSR models, including residual blocks, recurrent networks, and upsampling networks.
  arXiv  Detail & Related papers  (2022-06-15T17:36:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.