Camera Image Evaluation With Reference Images for Storage Reduction
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Solution Overview
Problem
Amateur photographers face challenges in capturing high-quality images that require significant storage space due to the need to take multiple shots and later select the best ones, leading to increased storage requirements.
Innovation Solution
A computer-implemented method for evaluating camera images by comparing image features with a group of reference images, determining an evaluation parameter, and providing real-time feedback or instructions to improve or discard images based on predefined thresholds, thereby reducing the number of captured images and storage needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple camera shots are taken to ensure high-quality images, then image quality is improved, but storage space requirements increase
Solution Approach 1:
The system performs preliminary evaluation of camera images using AI/ML models to predict quality metrics before the user reviews or stores the images. This preliminary quality assessment allows the system to identify and retain only high-quality images, eliminating the need to store multiple mediocre shots while ensuring at least one excellent image is captured and saved.
2Manufacturing precision
If multiple camera shots are taken to select the best image, then image quality is improved, but the number of captured images increases
Solution Approach 1:
The system implements feedback loops where AI/ML models continuously evaluate captured images against quality criteria and provide real-time feedback on image quality metrics. This feedback mechanism enables the camera to automatically determine when a sufficient quality image has been captured, reducing the need to take excessive numbers of shots while maintaining high image quality standards.
3Manufacturing precision
If manual selection of best images is performed, then image quality is improved, but time consumption increases
Solution Approach 1:
The system employs self-service automation through AI/ML-based quality assessment that automatically evaluates and ranks captured images without requiring manual user intervention. The system independently determines which images meet quality criteria and should be retained, freeing the user from time-consuming manual review while ensuring high-quality image selection.
4Reliability
If all captured images are stored, then no quality filtering is lost, but storage efficiency decreases
Solution Approach 1:
The system changes the evaluation parameter from binary (keep/delete) to a quality score spectrum, where images are assessed on multiple dimensions (composition, lighting, subject recognition, etc.). This parameter transformation enables nuanced quality filtering that retains only images meeting specific quality thresholds, improving storage efficiency while maintaining reliability through multi-criteria assessment.
Data Source
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AI summary
A computer-implemented method for evaluating a camera image, comprising the following steps: providing the camera image, identifying at least one image feature of the camera image, determining a group of reference images based on the image feature, using at least one reference image feature for the group of reference images, comparing the image feature and the reference image feature, and calculating an evaluation parameter for the camera image based on the comparison between the image feature and the reference image feature.