Confidence-Based Motion Vector Selection for Video Encoding
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Solution Overview
Problem
Conventional video encoding systems face complexity in motion vector selection and optimization due to the increasing number of motion vectors, which can lead to difficulties in hardware constraints and inefficient motion compensation.
Innovation Solution
A system and method for motion compensation that utilize confidence-based motion vector selection, characterizing residual values using noise power estimates and combining metrics like SAD, SSD, and P-hat to rank motion vectors, and adjust confidence based on noise estimation to improve motion vector accuracy and reduce artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of motion vectors increases to improve motion compensation accuracy, then motion compensation efficiency is improved, but device complexity and optimization difficulty increase
Solution Approach 1:
The patent segments the motion vector selection process into multiple stages: generating a limited set of candidate motion vectors from predefined sets, ranking these candidates using multiple metrics (SAD, SSD, P-hat), and selecting the best candidate. This segmentation transforms the complex problem of evaluating all possible motion vectors into a manageable multi-stage process with controlled candidate sets at each stage.
Solution Approach 2:
The patent dynamically adjusts the motion vector selection process by using confidence-based ranking that adapts to different video content characteristics. The system dynamically generates and ranks candidate motion vectors based on residual analysis and noise power estimation, allowing the selection process to adapt to varying scene complexity and motion patterns rather than using a fixed selection approach.
2Measurement precision
If multiple metrics (SAD, SSD, P-hat) are combined to rank motion vectors, then motion vector accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent merges multiple evaluation metrics (SAD - Sum of Absolute Differences, SSD - Sum of Squared Differences, and P-hat - noise-based metric) into a unified confidence-based ranking system. These metrics are computed for each candidate motion vector and combined to generate an overall confidence score, allowing the system to leverage the strengths of each metric while maintaining a structured evaluation framework.
Solution Approach 2:
The patent implements feedback mechanisms where the ranking process uses confidence scores derived from multiple metrics to iteratively refine motion vector selection. The system computes residuals, estimates noise power, and uses this feedback to adjust the ranking of candidate motion vectors, creating a closed-loop evaluation process that improves selection accuracy through iterative refinement.
Data Source
AI summary
Described herein is a method and system for motion compensation. This system and method use confidence for motion vector selection. Relative cost and an estimate of noise power may be used to characterize residual values and influence confidence determination.