Affine Merge Model for Video Coding Motion Vector Prediction
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Solution Overview
Problem
Current video coding technologies face challenges in efficiently reducing redundancy in video signals, particularly in motion vectors, which affects compression ratios and bandwidth requirements, especially in high-resolution video applications.
Innovation Solution
The method involves using an affine merge model for video decoding, where prediction information from neighboring blocks is used to derive candidate motion vectors for control points, allowing for transformation between the current block and a reference block, thereby reducing the need for direct coding of motion vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If motion vectors are directly coded without prediction, then coding accuracy is maintained, but data requirements and bandwidth increase significantly
Solution Approach 1:
The patent applies motion vector prediction by copying motion vector values from neighboring blocks (spatial prediction) and from previously decoded blocks (temporal prediction). This allows the current block's motion vector to be derived from existing data rather than coded directly, significantly reducing the number of bits required while maintaining acceptable prediction accuracy for most video sequences.
Solution Approach 2:
The patent introduces an intermediary prediction mechanism that uses neighboring blocks as mediators to estimate motion vectors. Instead of directly coding motion vectors, the system uses prediction from adjacent spatial blocks and temporal references as intermediaries to reconstruct motion information, reducing data requirements while preserving essential motion characteristics.
2Loss of energy
If compression ratio is increased through aggressive redundancy reduction, then bandwidth and storage requirements decrease, but image quality and reconstruction accuracy deteriorate
Solution Approach 1:
The patent applies different prediction strategies to different blocks based on their characteristics. Complex blocks with high motion variability use more accurate prediction methods, while simple blocks use basic prediction. This local adaptation allows aggressive compression in homogeneous regions while maintaining quality in complex regions, optimizing the balance between compression ratio and reconstruction quality.
Solution Approach 2:
The patent dynamically adjusts prediction parameters and modes based on local video characteristics. The system changes prediction strength, reference block selection, and interpolation filters according to the specific block content and motion patterns, enabling optimized compression that adapts to varying quality requirements across different video regions.
Data Source
AI summary
Prediction information of a current block is decoded. The prediction information indicates an affine merge model, and the current block includes control points. A first candidate control point motion vector (CPMV) for a first control point of the control points is determined based on one of at least two CPMVs of a first neighboring block of the first control point and a translational motion vector of a second neighboring block of the first control point. Affine merge candidates for the current block are derived based on candidate CPMVs for the control points of the current block. The candidate CPMVs of the control points includes the first candidate CPMV. Parameters of the affine merge model are determined based on one of the affine merge candidates that is defined according to the prediction information. At least a sample of the current block is reconstructed according to the affine merge model.