Active Sampling for Ultra-Low-Bit-Rate Video Compression via Conditional Controlled Diffusion

• URL: http://arxiv.org/abs/2605.02849v1
• Date: Mon, 04 May 2026 17:25:14 GMT
• Title: Active Sampling for Ultra-Low-Bit-Rate Video Compression via Conditional Controlled Diffusion
• Abstract summary: We present ActDiff-VC, a diffusion-based video compression framework for the ultra-low-bitrate regime.<n>Our method partitions videos into variable-length segments, transmitss only when needed, and summarizes temporal dynamics using a compact set of tracked point trajectories.<n>Conditioned on sparse signals, a conditional diffusion decoder synthesizes the remaining frames, enabling perceptually realistic reconstruction under severe rate constraints.
• Score: 8.8956772305056
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion models provide a powerful generative prior for perceptual reconstruction at ultra-low bitrates, but effective video compression requires controlling the generative process using highly compact conditioning signals. In this work, we present ActDiff-VC, a diffusion-based video compression framework for the ultra-low-bitrate regime. Our method partitions videos into variable-length segments, transmits keyframes only when needed, and summarizes temporal dynamics using a compact set of tracked point trajectories. Conditioned on these sparse signals, a conditional diffusion decoder synthesizes the remaining frames, enabling perceptually realistic reconstruction under severe rate constraints. To support this design, we introduce two mechanisms: content-adaptive keyframe selection and budget-aware sparse trajectory selection, which together enable compact yet effective conditioning for generative reconstruction. Experiments on the UVG and MCL-JCV benchmarks show that ActDiff-VC achieves up to 64.6\% bitrate reduction at matched NIQE, improves KID by up to 64.6\% and FID by up to 37.7\% at comparable bitrates against strong learned codecs, and delivers favorable perceptual rate--distortion trade-offs relative to learned and diffusion-based baselines in the ultra-low-bitrate regime.

Related papers

• Group-of-Latents: Perceptual Video Compression at Extreme Bitrates via Masked Latent Generative Modeling [49.11379121512358]
  We propose a unified generative framework that leverages pre-trained Diffusion Transformer priors to achieve high perceptual quality at extremely lows.<n>Our method achieves state-of-the-art perceptual fidelity with rich spatial details and robust temporal consistency.
  arXiv  Detail & Related papers  (2026-07-21T06:36:32Z)
• Efficient Video Diffusion with Sparse Information Transmission for Video Compression [24.272279529510772]
  Video compression aims to maximize reconstruction quality with minimal distortions.<n>At ultra-lows, traditional end-to-end compression models tend to produce blurry images of poor quality.<n>We propose Diff-SIT to establish a new state-of-the-art in perceptual quality and temporal consistency.
  arXiv  Detail & Related papers  (2026-03-19T05:15:29Z)
• Enhancing Neural Video Compression of Static Scenes with Positive-Incentive Noise [51.028614105626154]
  Static scene videos, such as surveillance feeds and videotelephony streams, constitute a dominant share of storage consumption and network traffic.<n>Traditional standardized codecs and neural video compression (NVC) methods struggle to encode these videos efficiently due to inadequate usage of temporal redundancy and severe distribution gaps between training and test data.<n>We propose to incorporate positive-incentive noise into NVC for static scene videos, where short-term temporal changes are reinterpreted as positive-incentive noise to facilitate model finetuning.
  arXiv  Detail & Related papers  (2026-03-06T09:47:25Z)
• Free-GVC: Towards Training-Free Extreme Generative Video Compression with Temporal Coherence [30.812937732503457]
  Free-GVC is a training-free generative video compression framework.<n>Our method operates at the group-of-pictures level, encoding video segments into a compact latent space.<n>Experiments show that Free-GVC achieves an average of 93.29% BD-Rate reduction in DISTS over the latest neural DCVC-RT.
  arXiv  Detail & Related papers  (2026-02-10T15:12:51Z)
• MTC-VAE: Multi-Level Temporal Compression with Content Awareness [54.85288415164888]
  Latent Video Diffusion Models (LVDMs) rely on Variational Autoencoders (VAEs) to compress videos into compact latent representations.<n>We present a technique to convert fixed compression rate VAEs into models that support multi-level temporal compression.
  arXiv  Detail & Related papers  (2026-02-01T17:08:02Z)
• FLaTEC: Frequency-Disentangled Latent Triplanes for Efficient Compression of LiDAR Point Clouds [52.997038111673966]
  FLaTEC is a frequency-aware compression model that enables the compression of a full scan with high compression ratios.<n>We convert voxelized embeddings into triplane representations to reduce sparsity, computational cost, and storage requirements.<n>Our method achieves state-of-the-art rate-distortion performance and outperforms the standard codecs by 78% and 94% in BD-rate on both datasets.
  arXiv  Detail & Related papers  (2025-11-25T08:37:49Z)
• 4DGCPro: Efficient Hierarchical 4D Gaussian Compression for Progressive Volumetric Video Streaming [52.76837132019501]
  We introduce 4DGCPro, a novel hierarchical 4D compression framework.<n>4DGCPro facilitates real-time mobile decoding and high-quality rendering via progressive volumetric video streaming.<n>We present an end-to-end entropy-optimized training scheme.
  arXiv  Detail & Related papers  (2025-09-22T08:38:17Z)
• Single-step Diffusion for Image Compression at Ultra-Low Bitrates [19.76457078979179]
  We propose a single-step diffusion model for image compression that delivers high perceptual quality and fast decoding at ultra-lows.<n>Our approach incorporates two key innovations: (i) Vector-Quantized Residual (VQ-Residual) training, which factorizes a structural base code and a learned residual in latent space.<n>Ours achieves comparable compression performance to state-of-the-art methods while improving decoding speed by about 50x.
  arXiv  Detail & Related papers  (2025-06-19T19:53:27Z)
• REGEN: Learning Compact Video Embedding with (Re-)Generative Decoder [52.698595889988766]
  We present a novel perspective on learning video embedders for generative modeling.<n>Rather than requiring an exact reproduction of an input video, an effective embedder should focus on visually plausible reconstructions.<n>We propose replacing the conventional encoder-decoder video embedder with an encoder-generator framework.
  arXiv  Detail & Related papers  (2025-03-11T17:51:07Z)
• 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)
• Spatial Degradation-Aware and Temporal Consistent Diffusion Model for Compressed Video Super-Resolution [25.615935776826596]
  Due to storage and bandwidth limitations, videos transmitted over the Internet often exhibit low quality, characterized by low-resolution and compression artifacts.<n>Although video super-resolution (VSR) is an efficient video enhancing technique, existing VS methods focus less on compressed videos.<n>We propose a novel method that exploits the priors of pre-trained diffusion models for compressed VSR.
  arXiv  Detail & Related papers  (2025-02-11T08:57:45Z)

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.