AI Image Processing with Tag Expansion for Accurate Generation
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Solution Overview
Problem
Intelligent models create images that have a significant discrepancy from user needs due to general descriptive language input, requiring repeated modifications, leading to a poor user experience.
Innovation Solution
An image processing method that constructs a first image, processes a target image to obtain a first tag, expands the tag to a second tag with more content and different tags, and generates a second image based on the expanded tag to enhance accuracy and user satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If general descriptive language is used to input image generation, then the input process is simple and fast, but the generated image has large difference from user needs requiring repeated modifications
Solution Approach 1:
The system automatically extracts tags from the generated image and uses these tags as feedback to refine the prompt. The extracted tags are fed back into the generation process to create iterations that progressively align with user needs, resolving the contradiction between simple input and accurate output.
Solution Approach 2:
The system performs preliminary tag extraction and image processing before the final image generation. By pre-processing the input description into structured tags and preparing multiple iteration cycles, the system ensures that the final generated image accurately reflects user needs without requiring repeated manual input modifications.
2Manufacturing precision
If repeated modifications of descriptive language are made to improve image accuracy, then the image accuracy improves, but the user experience deteriorates due to multiple modification steps
Solution Approach 1:
The system performs self-service by automatically extracting tags from generated images and using these tags to refine subsequent generations. This autonomous tag extraction and utilization process eliminates the need for repeated manual prompt modifications, allowing the system to self-optimize image accuracy without user intervention.
Solution Approach 2:
The tag extraction and image generation process continues iteratively without interruption. The extracted tags from one iteration become the basis for the next iteration, creating a continuous improvement cycle that maintains useful action throughout the process until the desired image accuracy is achieved, eliminating idle modification steps.
3Manufacturing precision
If tag expansion is performed to increase content amount and diversity, then the image feature accuracy improves, but the processing complexity increases
Solution Approach 1:
The tag expansion process is segmented into discrete steps: extracting initial tags, expanding tags to increase content amount, filtering tags for diversity, and using expanded tags for image generation. This segmentation makes the complex processing manageable and systematic, breaking down the tag expansion into manageable phases that progressively improve image feature accuracy.
Data Source
AI summary
An image processing method includes constructing a first image according to a first input content, processing a target image of the first image to obtain a first tag representing an image feature, expanding the first tag to obtain a second tag, and obtaining a second image at least according to the second tag. The first image includes at least one frame. A content amount of the second tag is greater than a content amount of the first tag, and the second tag includes a tag different from the first tag.