mlsanityData to 14 Oct 2026

IBVC: Interpolation-driven B-frame Video Compression

arXiv 2309.13835 ↗First posted 25 Sep 2023eess.IV cs.CVcited by 12

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Abstract (arXiv)

Learned B-frame video compression aims to adopt bi-directional motion estimation and motion compensation (MEMC) coding for middle frame reconstruction. However, previous learned approaches often directly extend neural P-frame codecs to B-frame relying on bi-directional optical-flow estimation or video frame interpolation. They suffer from inaccurate quantized motions and inefficient motion compensation. To address these issues, we propose a simple yet effective structure called Interpolation-driven B-frame Video Compression (IBVC). Our approach only involves two major operations: video frame interpolation and artifact reduction compression. IBVC introduces a bit-rate free MEMC based on interpolation, which avoids optical-flow quantization and additional compression distortions. Later, to reduce duplicate bit-rate consumption and focus on unaligned artifacts, a residual guided masking encoder is deployed to adaptively select the meaningful contexts with interpolated multi-scale dependencies. In addition, a conditional spatio-temporal decoder is proposed to eliminate location errors and artifacts instead of using MEMC coding in other methods. The experimental results on B-frame coding demonstrate that IBVC has significant improvements compared to the relevant state-of-the-art methods. Meanwhile, our approach can save bit rates compared with the random access (RA) configuration of H.266 (VTM). The code will be available at https://github.com/ruhig6/IBVC.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 46 of its 66 references that are in the map. First posted on arXiv 25 Sep 2023.

Builds on

46 of 66 references.

References · 46

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34 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 7; 2026: 3.

12 papers.

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Close in wording, no citation either way

  1. 2026DCVC-MB: Neural B-Frame Video Compression using State Space Models
  2. 2026NeuralLVC: Neural Lossless Video Compression via Masked Diffusion with Temporal Conditioning
  3. 2026High-Fidelity Video Compression based on Invertible Neural Transform and Implicit Conditioning
  4. 2026Geometric Transformation-Embedded Mamba for Learned Video Compression
  5. 2024M3-CVC: Controllable Video Compression with Multimodal Generative Models
  6. 2025MiVID: Multi-Strategic Self-Supervision for Video Frame Interpolation using Diffusion Model
  7. 2022Multi-rate adaptive transform coding for video compression
  8. 2026DiV-INR: Extreme Low-Bitrate Diffusion Video Compression with INR Conditioning