Dynamic Motion Vector Range Selection for Video Encoding
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Solution Overview
Problem
Existing video compression techniques face inefficiencies in motion estimation, particularly in adapting motion vector ranges and search ranges to varying motion characteristics within video content, leading to suboptimal rate-distortion performance and increased computational costs.
Innovation Solution
A video encoder dynamically selects motion vector ranges and search ranges based on motion vector distribution information, using histogram analysis to constrain motion estimation and adjust ranges accordingly, thereby improving performance across different motion levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a large motion vector range is used in motion estimation, then the quality of motion compensation is improved, but the computational complexity and encoding time increase significantly
Solution Approach 1:
The patent implements dynamic adjustment of motion vector range based on scene complexity detection. The encoder analyzes motion activity in reference frames and adaptively selects motion vector range (e.g., range 0 for low motion, range 1 for medium motion, range 2 for high motion) on a per-picture or per-block basis. This dynamic approach ensures large motion vector ranges are used only when necessary, reducing computational complexity while maintaining motion compensation quality for high-motion scenes.
Solution Approach 2:
The patent changes the motion vector range parameter based on detected motion characteristics. By modifying this critical parameter adaptively, the system optimizes the balance between motion compensation accuracy and computational load. The motion vector range parameter controls the search area size, and its dynamic adjustment allows the encoder to match computational resources to actual scene requirements.
2Manufacturing precision
If a large search range is used in motion estimation, then better motion matches are found, but the encoding time and computational resources increase
Solution Approach 1:
The patent dynamically adjusts search range based on motion activity detection. For pictures with low motion characteristics, a reduced search range is applied, significantly decreasing the number of candidate blocks evaluated. For high-motion pictures, the full search range is used to ensure accurate motion compensation. This dynamic search range adjustment directly improves encoding speed while preserving motion estimation accuracy when needed.
Solution Approach 2:
The patent modifies search range parameters adaptively based on scene analysis. By changing search range size as a function of detected motion characteristics, the system optimizes the trade-off between finding accurate motion matches and maintaining high encoding throughput. The parameter adjustment ensures computational resources are proportional to actual scene complexity.
3Device complexity
If motion vector range is fixed for all pictures, then encoding complexity is reduced, but rate-distortion performance suffers in scenes with varying motion characteristics
Solution Approach 1:
The patent implements dynamic motion vector range selection that adapts to scene characteristics. The encoder detects motion activity levels and adjusts motion vector range accordingly, using smaller ranges for static scenes and larger ranges for high-motion scenes. This dynamic approach improves rate-distortion performance by matching encoding parameters to actual content requirements, rather than using a fixed conservative setting that would degrade performance in low-motion scenes.
Solution Approach 2:
The patent changes motion vector range parameters based on analyzed motion characteristics of each picture or block. This parameter adaptation allows the encoder to optimize rate-distortion performance for varying scene types while maintaining manageable encoding complexity through automated detection and selection logic.
4Adaptability or versatility
If motion vector range is increased to handle extended motion, then coverage of large displacements is improved, but the computational cost of motion estimation increases
Solution Approach 1:
The patent dynamically selects motion vector range based on detected motion characteristics, enabling extended motion coverage only when actually needed. The system detects scenes with large displacements and activates larger motion vector ranges (e.g., range 2 for extended motion) selectively. This dynamic activation maintains adaptability for handling various motion types while reducing computational cost by using smaller ranges for the majority of low-motion scenes.
Data Source
AI summary
Techniques and tools for selecting search ranges and/or motion vector ranges during motion estimation are described. For example, a video encoder performs motion estimation constrained by a first search range, which results in multiple motion vectors. The encoder computes motion vector distribution information for the motion vectors. To compute the distribution information, the encoder can track the motion vectors in a histogram and count how many of the motion vectors fall within each of multiple intervals for the distribution information. The encoder then selects a second search range and performs motion estimation constrained by the second search range. Selecting the second search range can include selecting a motion vector range, which in some cases in effect determines the second search range.