Scenario-Based Motion Estimation Search Range for Video Encoding
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Solution Overview
Problem
Existing motion estimation algorithms in video encoding are time-consuming and inefficient, particularly for high-definition videos, and suffer from inaccuracies due to complex scenarios and irregular data access, leading to blurring and block effects.
Innovation Solution
A video motion estimation method that classifies image frames into scenarios, extracts contour and color features of foreground objects, and determines a search range based on these features to narrow down the search area, using starting search points and color features to improve accuracy and reduce computation time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional motion estimation algorithms are used to search the entire search range, then motion estimation accuracy can be maintained, but the computation time and processing speed increase significantly
Solution Approach 1:
The patent divides the video frames into multiple scenario groups based on foreground object characteristics (contour features, color features, motion features). Each scenario group is processed with a customized search range and starting search point, segmenting the overall motion estimation task into smaller, more efficient sub-tasks that reduce computation time while maintaining accuracy.
Solution Approach 2:
The patent applies different search strategies to different scenario groups. For each scenario, the search range and starting search point are locally optimized based on the specific characteristics of that scenario's foreground objects. This local customization allows each region to be searched with appropriate precision without uniformly processing the entire frame, reducing overall computation time.
2Productivity
If the search range is reduced to improve processing speed, then computation time decreases, but motion estimation accuracy may deteriorate
Solution Approach 1:
The patent performs preliminary analysis of foreground objects (extracting contour features, color features, and motion features) before the actual motion estimation search. Based on this preliminary information, it pre-determines the optimal search range and starting search point for each scenario group. This preliminary action ensures that the reduced search range still contains the target object, maintaining accuracy while improving processing speed.
Solution Approach 2:
The patent dynamically changes search parameters (search range size, starting search point position) based on the characteristics of different scenario groups. By adapting these parameters to match the specific features of each scenario (such as object size, color distribution, motion pattern), the system achieves high processing speed without sacrificing motion estimation accuracy.
3Productivity
If scenario classification and feature extraction are performed, then search efficiency improves, but the complexity of the motion estimation system increases
Solution Approach 1:
The patent segments the motion estimation system into distinct modules: foreground object detection, feature extraction (contour, color, motion), scenario classification, and customized motion estimation. This modular segmentation allows each component to be independently optimized and managed, improving overall search efficiency while making the system complexity more manageable through clear functional separation.
Solution Approach 2:
The patent creates a universal framework that handles multiple scenario types through a common architecture. The feature extraction and classification mechanisms work across different video content types, and the scenario-based approach can adapt to various foreground object characteristics. This multi-functionality improves search efficiency across diverse video content without requiring completely separate systems for each scenario.
Data Source
AI summary
A video motion estimation method including obtaining a plurality of image frames in a video, and performing scenario classification processing on the plurality of image frames to obtain a plurality of image frame sets. The method further includes extracting a contour feature and a color feature of a foreground object of each image frame, and determining a search range corresponding to each image frame set. The method further includes determining a starting search point in each predicted frame. The method further includes, for each image frame set, performing motion estimation processing in a search region corresponding to the search range of each predicted frame set based on the starting search point of the respective predicted frame, a reference block in at least one reference frame of the respective image frame set, and the color feature of the foreground object, to obtain a motion vector corresponding to the reference block.