Uniformly Accelerated Motion Model for Inter Prediction
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Abstract (arXiv)
Inter prediction is a key technology to reduce the temporal redundancy in video coding. In natural videos, there are usually multiple moving objects with variable velocity, resulting in complex motion fields that are difficult to represent compactly. In Versatile Video Coding (VVC), existing inter prediction methods usually assume uniform speed motion between consecutive frames and use the linear models for motion estimation (ME) and motion compensation (MC), which may not well handle the complex motion fields in the real world. To address these issues, we introduce a uniformly accelerated motion model (UAMM) to exploit motion-related elements (velocity, acceleration) of moving objects between the video frames, and further combine them to assist the inter prediction methods to handle the variable motion in the temporal domain. Specifically, first, the theory of UAMM is mentioned. Second, based on that, we propose the UAMM-based parameter derivation and extrapolation schemes in the coding process. Third, we integrate the UAMM into existing inter prediction modes (Merge, MMVD, CIIP) to achieve higher prediction accuracy. The proposed method is implemented into the VVC reference software, VTM version 12.0. Experimental results show that the proposed method achieves up to 0.38% and on average 0.13% BD-rate reduction compared to the VTM anchor, under the Low-delay P configuration, with a slight increase of time complexity on the encoding/decoding side.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 5 of its 20 references that are in the map. First posted on arXiv 16 Jul 2024.
Builds on
5 of 20 references.
References · 5
- 2019Quadratic video interpolationcited by 143
- 2020Enhanced Quadratic Video Interpolationcited by 48
- 2024Object Segmentation-Assisted Inter Prediction for Versatile Video Codingcited by 22
- 2022Advancing Learned Video Compression with In-loop Frame Predictioncited by 14
- 2017An Efficient Four-Parameter Affine Motion Model for Video Codingcited by 10
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Citing papers per year: 2024: 1; 2025: 9.
10 papers.
- 2024USTC-TD: A Test Dataset and Benchmark for Image and Video Coding in 2020scited by 37
- 2025StableCodec: Taming One-Step Diffusion for Extreme Image Compressioncited by 29
- 2025Learned Image Compression with Hierarchical Progressive Context Modelingcited by 20
- 2025Neural Video Compression with Context Modulationcited by 15
- 2025RobustGS: Unified Boosting of Feedforward 3D Gaussian Splatting under Low-Quality Conditionscited by 13
- 2025Augmented Deep Contexts for Spatially Embedded Video Codingcited by 8
- 2025Real-Time Neural Video Compression with Unified Intra and Inter Codingcited by 3
- 2025Partition Map-Based Fast Block Partitioning for VVC Inter Codingcited by 1
- 2025Scaling Learned Image Compression Models up to 1 Billioncited by 1
- 2025FCA2: Frame Compression-Aware Autoencoder for Modular and Fast Compressed Video Super-Resolutioncited by 0
- 2025Real-Time Neural Video Compression with Unified Intra and Inter Coding16 Oct 2025
- 2025Scaling Learned Image Compression Models up to 1 Billion12 Aug 2025
- 2025RobustGS: Unified Boosting of Feedforward 3D Gaussian Splatting under Low-Quality Conditions5 Aug 2025
- 2025Learned Image Compression with Hierarchical Progressive Context Modeling25 Jul 2025
- 2025StableCodec: Taming One-Step Diffusion for Extreme Image Compression27 Jun 2025
- 2025FCA2: Frame Compression-Aware Autoencoder for Modular and Fast Compressed Video Super-Resolution13 Jun 2025
- 2025Neural Video Compression with Context Modulation20 May 2025
- 2025Augmented Deep Contexts for Spatially Embedded Video Coding8 May 2025
- 2025Partition Map-Based Fast Block Partitioning for VVC Inter Coding25 Apr 2025
- 2024USTC-TD: A Test Dataset and Benchmark for Image and Video Coding in 2020s13 Sep 2024