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Uniformly Accelerated Motion Model for Inter Prediction
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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.
Forward citations
Cited by 2 Pith papers
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Real-Time Neural Video Compression with Unified Intra and Inter Coding
UI2C unifies intra and inter coding in one neural video codec and jointly encodes frame pairs, beating DCVC-RT by 12.1% BD-rate while keeping real-time speed.
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Augmented Deep Contexts for Spatially Embedded Video Coding
SEVC augments a temporal neural video codec with low-resolution spatial references, motion-feature co-augmentation, and a spatial-guided latent prior, achieving about 11.9% more bitrate saving than DCVC-FM.
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