DMVR Inter Prediction for Lower-Complexity Motion Refinement
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Solution Overview
Problem
The increasing demand for high-resolution and high-quality images and videos, particularly in immersive media formats like VR and AR, has led to higher transmission and storage costs due to the increased amount of information required, necessitating a high-efficiency image and video compression technology.
Innovation Solution
The implementation of decoder-side Motion Vector Refinement (DMVR) and Bi-directional Optical Flow (BDOF) in inter prediction methods to enhance image coding efficiency by refining motion vectors and determining application conditions based on block size and bi-prediction weight index information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution and high-quality image and video data are transmitted or stored using existing methods, then image quality is improved, but transmission costs and storage costs are increased
Solution Approach 1:
The patent extracts and removes redundant information from high-resolution image and video data through advanced compression algorithms. By identifying and eliminating duplicate or unnecessary data elements, the system maintains high image quality while significantly reducing the total amount of information that needs to be transmitted or stored, thereby lowering transmission and storage costs.
Solution Approach 2:
The patent applies preliminary compression and optimization processes to image and video data before transmission or storage. By pre-processing the data to remove redundancies and optimize the representation, the system reduces the amount of information that needs to be handled later, while preserving the essential quality characteristics of the high-resolution content.
2Measurement precision
If decoder-side motion vector refinement (DMVR) is applied to all blocks, then prediction accuracy is improved, but calculation complexity is increased
Solution Approach 1:
The patent applies DMVR selectively to specific blocks rather than uniformly to all blocks. By analyzing local characteristics such as block size, motion complexity, and prediction mode, the system applies refinement only where it provides significant benefit, thereby maintaining high prediction accuracy for critical regions while reducing overall calculation complexity for the entire picture.
Solution Approach 2:
The patent changes the application parameters of DMVR based on block-specific conditions. By adjusting whether DMVR is applied, the refinement strength, or the search range based on parameters like block size and motion vector characteristics, the system optimizes the balance between prediction accuracy and computational load for different regions of the image or video.
Data Source
AI summary
An image decoding method performed by a decoding device according to the present document comprises the steps of: deriving a motion vector for a current block; determining whether to apply refinement to the motion vector for the current block; deriving a refined motion vector by applying decoder-side motion vector refinement (DMVR) to the motion vector if it is determined to apply refinement to the motion vector for the current block; deriving prediction samples for the current block on the basis of the refined motion vector; and generating reconstruction samples for the current block on the basis of the prediction samples, wherein the step of determining whether to apply refinement includes determining whether to apply the refinement on the basis of at least one of the size of the current block and bi-prediction weight index information for the current block.