Image Conversion Model Compatibility Detection for Meaning Preservation
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Solution Overview
Problem
Existing image conversion systems using machine learning models are prone to incompatibility issues, leading to unintended deformation of objects and changes in image meaning, especially when the input image significantly differs from the training data, affecting the accuracy of image-based inspections.
Innovation Solution
An incompatibility detection device and method that includes an image conversion unit, incompatibility detection unit, and storage unit to determine if an image is compatible with the learning model by comparing the image's evaluation values to a model-compatible region, and reports incompatibility, with countermeasures such as model retraining or changing the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a learning model is used for image conversion processing to improve image quality, then noise removal and aberration improvement effects are achieved, but unexpected secondary image conversion processing occurs causing object deformation and meaning change
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing compatibility evaluation values for all possible input image characteristics during the model training phase. These pre-computed compatibility thresholds are stored in a table structure, allowing the system to quickly determine model compatibility without performing complex calculations during actual image processing, thus preventing deformation issues before they occur.
Solution Approach 2:
The patent implements feedback by calculating a compatibility evaluation value that compares the input image characteristics against the training data distribution. When the compatibility value falls below a predetermined threshold, the system provides feedback to either select a different pre-trained model or trigger retraining with appropriate data, thereby preventing unreliable image conversions that would distort object meaning.
2Ease of operation
If visual selection of learning model is used for noise removal, then ease of model selection is improved, but detection of incompatibility and unexpected deformation is not possible
Solution Approach 1:
The patent introduces an intermediary compatibility evaluation mechanism that automatically assesses whether a selected learning model is appropriate for the given input image. This intermediary layer (compatibility evaluation unit) mediates between the user's model selection and the actual image processing, providing an automated check that detects incompatibility without requiring the user to have expert knowledge or perform complex visual assessments.
3Measurement precision
If learning model converts input image to approach training material data, then noise removal effect is achieved, but object deformation occurs when input image differs greatly from training data
Solution Approach 1:
The patent applies parameter changes by monitoring the compatibility evaluation value that reflects the statistical distance between input image characteristics and training data distribution. When this parameter (compatibility value) falls below a threshold, the system changes the processing approach by either selecting a different pre-trained model with appropriate compatibility or triggering retraining, thus preventing the harmful parameter change of object deformation while maintaining beneficial noise removal.
Data Source
AI summary
An incompatibility detection unit comprises: an image conversion unit that converts an input low-quality image into a corresponding high-quality image using a learning model; an incompatibility detection unit that detects whether or not the input low-quality image is incompatible with the learning model; an incompatibility reporting unit that reports detected incompatibility; and a storage unit that stores, as a model-compatible region, the distribution of evaluation values of high-quality correct images used in the training stage of the learning model, in association with the learning model. The incompatibility detection unit determines that the learning model is incompatible when an evaluation value of the input low-quality image is not within the model-compatible region.