Video Motion Estimation Using Segmented Prediction Units
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Solution Overview
Problem
Existing video motion estimation methods are inefficient due to the use of a uniform processing method for all prediction units, leading to inflexible and computationally complex motion estimation processes.
Innovation Solution
The method divides image frames into two types of prediction units, applying different search algorithms to each type to obtain motion vectors, utilizing polygonal models and advanced motion vector prediction techniques for more efficient motion estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a uniform processing method is used for all prediction units, then the motion estimation process is simplified, but the efficiency and flexibility of motion estimation deteriorates
Solution Approach 1:
The patent segments prediction units into different types (first type and second type) and applies different search algorithms to each type. This segmentation allows the system to optimize motion estimation for each category separately, improving overall efficiency while maintaining manageable complexity through structured classification.
Solution Approach 2:
The patent applies different search algorithms (first search algorithm for first type, second search algorithm for second type) to different prediction units based on their characteristics. This local quality approach ensures that each prediction unit receives the most appropriate processing method, enhancing motion estimation efficiency without requiring complex uniform processing for all units.
2Measurement precision
If a new independent search is performed on each prediction unit, then the motion estimation is thorough, but the processing time and computational complexity increases
Solution Approach 1:
By dividing prediction units into different types and applying specialized search algorithms to each type, the patent reduces redundant searches while maintaining thoroughness. Each segment receives targeted processing that is both accurate and time-efficient.
Solution Approach 2:
The patent changes search parameters (different search algorithms) based on prediction unit type characteristics. This allows the system to adjust the thoroughness and computational effort according to the specific needs of each prediction unit type, optimizing the balance between accuracy and processing time.
3Stability of the object's composition
If the same search complexity is used for all prediction units, then the processing is consistent, but the flexibility and efficiency of motion estimation deteriorates
Solution Approach 1:
The patent segments prediction units into different types with different search algorithms, allowing each segment to have optimized processing consistency appropriate to its characteristics. This maintains overall system consistency through structured classification while enabling flexibility in how each segment is processed.
Solution Approach 2:
Different search algorithms are applied to different prediction unit types based on their local characteristics. This local quality approach provides flexibility and adaptability for each prediction unit type while maintaining processing consistency within each category through dedicated algorithms.
Data Source
AI summary
A terminal performs video estimation by dividing an image frame of a video into a plurality of prediction units, and dividing the plurality of prediction units into a first type of prediction units and a second type of prediction units. A motion vector of a prediction unit of the first type is then obtained according to a first search algorithm. A motion vector of a prediction unit of the second type is obtained according to a second search algorithm. The second search algorithm is different from the first search algorithm. Then sub-pixel motion estimation is performed on the image frame according to the motion vector of the prediction unit of the first type and the motion vector of the prediction unit of the second type to generate a motion estimate result.