Emotional Image Quality Score for Automated Photo Selection
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Solution Overview
Problem
Conventional automated image selection methods fail to effectively identify the best image from multiple photos taken at the same time, often retaining duplicate photos and wasting time in the process, as they do not adequately consider emotional and relationship factors between the photographer and the objects in the images.
Innovation Solution
A computer-implemented method that collects biometric, emotion, and context data from images, calculates an Overall Emotional Image Quality Score (OEIQS) based on image quality, emotion, and relationship values, and categorizes images to automatically select and manage the best photos, dynamically removing duplicates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional automated image selection based on image quality is used, then image quality is improved, but the selection accuracy fails to identify the best image when multiple photos are captured at the same time
Solution Approach 1:
The patent extends the selection criteria from traditional image quality parameters alone to a composite scoring system that incorporates multiple parameters including image quality metrics, emotional analysis data, and contextual information. This multi-parameter approach resolves the contradiction by maintaining precise image quality measurement while improving overall selection accuracy through additional distinguishing factors.
Solution Approach 2:
The patent segments the image selection process into multiple independent evaluation components: image quality assessment, emotional analysis, and contextual evaluation. Each component is measured and scored separately, then combined into an overall Emotional Image Quality Score (EIQS). This segmentation allows precise measurement in each domain while improving reliability through the combination of multiple assessment dimensions.
2Reliability
If photographers manually review and compare multiple photos to select the best image, then selection accuracy is improved, but time consumption increases significantly
Solution Approach 1:
The patent implements an automated system that performs image selection without requiring photographer intervention. The system self-evaluates multiple photos using emotional analysis and contextual data, automatically generating EIQS scores and identifying the best images. This self-service approach maintains high selection accuracy while eliminating the time-consuming manual review process.
Solution Approach 2:
The patent replaces the mechanical process of manual photo review and comparison with an automated computational system. Instead of photographers physically examining and comparing images, the system uses algorithms to analyze emotional content, contextual metadata, and image quality metrics, substituting human manual labor with automated processing that achieves comparable or superior accuracy without time loss.
3Reliability
If photographers retain duplicate photos to avoid missing ideal moments, then image coverage is improved, but storage space is wasted and management becomes difficult
Solution Approach 1:
The patent changes the parameter used to determine photo retention from simple duplication detection to a comprehensive EIQS-based evaluation. Instead of keeping all duplicates, the system calculates EIQS scores for each photo and selectively retains only those with high scores, using emotional and contextual parameters to distinguish valuable images from redundant ones. This maintains image coverage completeness while reducing storage consumption.
4Speed
If conventional image selection systems are used, then processing speed is improved, but the ability to understand emotional and relationship factors is lost
Solution Approach 1:
The patent creates a multi-functional system that simultaneously performs rapid image processing and deep emotional analysis. The system processes multiple photos quickly while also evaluating emotional content, relationship contexts, and situational factors. This universal approach maintains processing speed by parallelizing operations while preventing information loss through comprehensive multi-dimensional analysis.
Data Source
AI summary
According to one embodiment, a computer-implemented method includes collecting data corresponding to an image of one or more objects captured by a user on an image capture device, wherein the data comprises biometric data, emotion data, image quality data, and context data. In addition, the computer-implement method includes determining an image quality (IQ) value based on the image quality data, determining an emotion (E) value based on at least one of the biometric data and the emotion data, and determining a relationship (R) value between the one or more objects and the user based on the context data. The computer-implement method includes calculating an Overall Emotional Image Quality Score (OEIQS) for the image based on the IQ value, the E value, and the R value, and categorizing the image into at least one of a plurality of predefined categories.