Inter Prediction Motion Refinement for Video Coding Efficiency
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Solution Overview
Problem
The increasing demand for high-resolution and high-quality images and videos, along with immersive media formats, necessitates a high-efficiency image and video compression technology to reduce transmission and storage costs while maintaining quality.
Innovation Solution
Implementing decoder-side motion vector refinement (DMVR) and bi-directional optical flow (BDOF) for inter prediction, with conditions to determine their application, to enhance image coding efficiency and reduce complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution and high-quality image/video data are transmitted or stored using existing methods, then image quality is maintained, but transmission costs and storage costs increase due to increased data volume
Solution Approach 1:
The patent extracts and transmits only the essential residual information after motion compensation rather than the complete high-resolution video data. By separating the motion information from the full image data and only encoding the differences (residuals), the system maintains image quality while significantly reducing the quantity of data that needs to be transmitted or stored.
Solution Approach 2:
The patent changes the representation parameters of video data by transforming spatial domain image data into temporal domain motion vectors and residuals. This parameter transformation allows the same visual information to be represented more compactly, reducing data volume while preserving image quality through efficient motion compensation reconstruction.
2Measurement precision
If decoder-side motion vector refinement (DMVR) is applied to refine motion vectors, then prediction accuracy is improved, but calculation complexity increases
Solution Approach 1:
The patent applies partial refinement by performing DMVR only on selected motion vectors based on specific criteria (such as block size thresholds or motion complexity measures) rather than uniformly refining all motion vectors. This selective approach achieves sufficient prediction accuracy for complex regions while avoiding unnecessary calculations in simpler regions, thus balancing accuracy improvement with computational complexity management.
Data Source
AI summary
An image decoding method includes: acquiring, from a bitstream, luma weight L0 flag information indicating whether there is an L0 prediction-related weight factor and luma weight L1 flag information indicating whether there is an L1 prediction-related weight factor; determining to apply decoder-side motion vector refinement (DMVR) to an L0 motion vector and L1 motion vector for a current block, when the luma weight L0 flag information and the luma weight L1 flag information are both zero; when it has been determined to apply DMVR, deriving a refined L0 motion vector and a refined L1 motion vector by applying the DMVR to the current block; deriving prediction samples for the current block on the basis of L0 prediction using the refined L0 motion vector and L1 prediction using the refined L1 motion vector; and generating reconstruction samples for the current block on the basis of the prediction samples.