Blur Correction Evaluation Using Segmented Image Stability Analysis
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Solution Overview
Problem
Existing methods for evaluating blur correction capability in image capturing apparatuses fail to account for variations in blur amount over time, leading to inaccurate assessments due to fluctuations caused by camera shake, particularly when using gyro sensors with drift characteristics.
Innovation Solution
The method involves segmenting captured images into multiple segments, calculating average blur amounts for each segment, and using the largest average value as a determination criterion to evaluate blur correction capability, thereby accounting for stability and fluctuations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the blur correction capability is evaluated using only the average value for the entirety of captured images, then the evaluation process is simple, but the evaluation accuracy deteriorates when blur amount deviates with passage of time
Solution Approach 1:
The patent divides the captured images into multiple segments (first segment and second segment) based on the capture sequence. By calculating average blur amounts separately for each segment and comparing them, the method detects time-dependent blur variations that would be masked in a overall average calculation, thereby improving evaluation accuracy without excessive complexity
Solution Approach 2:
The patent introduces dynamic evaluation by comparing blur amounts across different time segments rather than using a static overall average. This dynamic approach captures changes in blur characteristics during the capture process, enabling detection of gyro drift effects that manifest as time-dependent blur variations
2Reliability
If the number of captured images is increased to 200 or more for adequate evaluation, then the evaluation comprehensiveness is improved, but the evaluation time increases
Solution Approach 1:
The patent extracts and compares specific segments of the captured images (first segment vs. second segment) rather than processing all 200+ images uniformly. This extraction approach focuses computational resources on the most informative portions of the dataset, maintaining evaluation reliability while reducing processing time by avoiding redundant calculations on the entire image set
Data Source
AI summary
The present invention provides an evaluation method for more precisely determining the blur correction capability of an image capturing apparatus. The method for evaluating the blur correction effect of the image capturing apparatus comprises acquiring a first change amount for each image capture from a predetermined captured number of subject images that have been captured by an image capturing apparatus that has been vibrated; dividing the predetermined number of captured images into a plurality of segments, and calculating a second change amount for each segment by performing statistical processing on the first change amounts in each of the plurality of segments; determining the largest second change amount from among the second change amounts in each segment to serve as a third change amount; and performing evaluation of the blur correction effect based on the third change amount.