Global Motion Estimation Using Weighted Neighboring Vectors
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Solution Overview
Problem
Existing global motion estimation technologies have low accuracy, especially when a moving object or noise is present in the image or the image quality is poor, making it difficult to effectively reduce image blurring.
Innovation Solution
A digital image processing apparatus and method that obtain template motion vectors for different areas of an image frame, correct their positions using a weighted sum of neighboring motion vectors, calculate scores based on confidence and number of neighboring vectors, and select a global motion vector for image stabilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional global motion estimation technology is used, then the processing is simple, but the estimation accuracy is low especially when moving objects or noise are present
Solution Approach 1:
The image frame is divided into multiple local regions, and motion vectors are calculated separately for each region. This segmentation allows the system to handle moving objects and noise more effectively by treating different areas independently, thereby improving global motion estimation accuracy without requiring overly complex global processing.
Solution Approach 2:
Different weighting strategies are applied to different local regions based on their characteristics. Regions with high motion variability or noise are assigned different weights compared to stable regions. This local quality approach enables the system to adapt to local image characteristics, improving estimation accuracy while maintaining reasonable processing complexity.
2Measurement precision
If more complex motion estimation methods are applied to improve accuracy, then estimation precision improves, but processing time and computational load increase
Solution Approach 1:
The system performs preliminary classification of local regions to identify areas with high motion variability, noise, or moving objects before conducting detailed motion estimation. This preliminary action allows the system to focus computational resources on critical regions, improving accuracy while reducing overall processing time by avoiding exhaustive analysis of all regions.
Solution Approach 2:
The system dynamically adjusts estimation parameters such as template size, search range, and weighting factors based on local image characteristics and motion patterns. By adapting parameters to local conditions rather than using fixed values, the system achieves higher accuracy in challenging regions while maintaining efficiency in simpler regions, thereby reducing overall processing time.
Data Source
AI summary
Provided are a digital image processing apparatus which corrects a position of each of a plurality of template motion vectors of an image frame of a captured image by considering relative positions of neighboring motion vectors respectively similar to each of the template motion vectors, calculates a score of each of the template motion vectors by considering a number of the neighboring motion vectors, and selects a global motion vector representing the image frame based on the score, and a method of estimating a global motion of an image to stabilize the captured image using the global motion.