Image Processing Candidate Selection for Reliable AI Prompt Execution
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Solution Overview
Problem
Existing image processing systems face the challenge of unintentionally executing unintended image processing due to the reliance on the accuracy of generative models and user prompts, leading to instability in achieving desired image processing outcomes.
Innovation Solution
An information processing system that acquires user input and image processing algorithm information, uses a deep learning-based generative model to suggest image processing candidates, and allows user acceptance or rejection, thereby stabilizing the execution of desired image processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a generative model based on deep learning is used to automatically execute image processing based on user prompts, then the automation and ease of operation are improved, but the reliability and stability of executing desired image processing deteriorate due to potential inaccuracies in model interpretation
Solution Approach 1:
The system displays image processing candidates to the user and receives feedback by detecting operations to select or reject candidates. This feedback loop allows the system to automatically execute the desired image processing while maintaining reliability through user confirmation, resolving the contradiction between automation and reliability.
2Productivity
If the system directly executes image processing based on generative model output, then the productivity and efficiency are improved, but the risk of unintended processing increases, reducing the quality of results
Solution Approach 1:
The system displays image processing candidates to the user before automatic execution. This preliminary action of showing candidates allows the user to review and confirm the processing to be executed, ensuring accuracy while maintaining efficient automatic execution, thus resolving the contradiction between productivity and precision.
3Manufacturing precision
If the system provides detailed control over image processing parameters, then the manufacturing precision and control over outcomes are improved, but the device complexity and difficulty of operation increase
Solution Approach 1:
The system introduces image processing candidates as an intermediary between the user's simple prompt input and the detailed parameter control. The generative model generates candidate parameters automatically, and the user selects from these candidates rather than directly controlling all parameters, thus maintaining precision while reducing operational complexity.
Data Source
AI summary
An information processing system that applies an image processing algorithm to image data includes a memory storing instructions, and at least one processor configured to execute the instructions to acquire input information from a user on the image data and information on the image processing algorithm, acquire an image processing algorithm corresponding to the input information and an image processing candidate being at least one of parameters constituting the image processing algorithm, by inputting a prompt based on the input information and the information on the image processing algorithm to a generative model based on deep learning, and display information on the image processing candidate on a display unit.