Dynamic Image Quality Evaluation via Adaptive Criteria
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Solution Overview
Problem
Existing image quality evaluation systems lack the ability to adapt to individual user preferences and criteria, leading to inconsistent and indiscriminate assessments.
Innovation Solution
An information processing system that allows evaluation criteria to be dynamically adjusted based on user-specific evaluation results, enabling exclusion or relaxation of evaluation items and criteria according to user needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If uniform evaluation criteria are applied to all images, then evaluation consistency is improved, but adaptability to individual user preferences deteriorates
Solution Approach 1:
The evaluation system dynamically adjusts evaluation criteria based on user preferences and historical evaluation data. The processor modifies evaluation weights and thresholds in real-time according to individual user needs, transforming static uniform criteria into dynamic adaptive criteria that maintain consistency within user-specific contexts while adapting across different users.
Solution Approach 2:
The system changes evaluation parameters such as weights assigned to different image quality attributes and threshold values based on user preferences. By modifying these parameters dynamically, the system maintains evaluation consistency through structured parameter management while achieving adaptability to individual user requirements.
2Adaptability or versatility
If evaluation criteria are customized for each user, then adaptability to user preferences is improved, but evaluation consistency deteriorates
Solution Approach 1:
The system implements parameter changes by adjusting evaluation weights and thresholds based on user preferences while maintaining a structured framework. This allows customization for each user through parameter modification rather than complete criterion redesign, preserving evaluation consistency through systematic parameter management.
Solution Approach 2:
The system uses feedback from user evaluations and preferences to continuously refine evaluation criteria. By incorporating feedback loops where user responses inform future evaluation adjustments, the system achieves both adaptability to individual users and consistency through learned patterns and standardized feedback processing.
3Measurement precision
If comprehensive evaluation items are used, then evaluation precision is improved, but device complexity deteriorates
Solution Approach 1:
The system extracts and focuses on the most relevant evaluation items based on user preferences and image characteristics. By selecting only necessary evaluation dimensions rather than applying all possible criteria, the system maintains high evaluation precision for relevant attributes while reducing overall system complexity through selective item extraction.
Solution Approach 2:
The evaluation system segments comprehensive evaluation into modular, independent evaluation items that can be selectively applied. This segmentation allows the system to maintain precision by evaluating only necessary aspects while reducing complexity through modular design that enables selective activation of evaluation components.
Data Source
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AI summary
An information processing system includes at least one processor configured to: obtain image data to be diagnosed; evaluate the image data on the basis of evaluation criteria for each of one or more evaluation items related to image quality; and allow an evaluation method based on the evaluation criteria to be changed according to an evaluation result in the evaluation of each of the evaluation items.