Image Style and Color Transformation Without Reference Images
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Solution Overview
Problem
Existing image transformation methods fail to effectively transform an input image to reflect both a specific style and color information without requiring a reference image, and do not allow users to control the degree of abstraction in the output image.
Innovation Solution
A pretrained transformation model is used to generate a texture-transformed image and a color-transformed image, which are then synthesized to create a result image that reflects the target style and color information, with user-controlled abstraction levels, using input images and target style and color information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a reference image is used for style transformation, then the style accuracy is improved, but the operation complexity and time consumption increase
Solution Approach 1:
The model performs style transformation autonomously using only the input image and text style description, eliminating the need for external reference images. The system self-determines the style transformation parameters based on the text description and applies them directly to the input image, achieving both style accuracy and operational simplicity.
Solution Approach 2:
The patent replaces the mechanical image-based reference system with a text-based semantic representation system. Instead of using visual reference images for style transfer, the system uses text descriptions that are converted into style parameters through a pretrained model, reducing operational complexity while maintaining style accuracy.
2Manufacturing precision
If detailed color information is applied to transform images, then the color accuracy is improved, but the processing time and computational resources increase
Solution Approach 1:
The model is pretrained in advance with extensive color transformation data and style information, storing the knowledge of how to transform images with detailed color requirements. During actual use, the system simply queries this prelearned knowledge rather than performing complex real-time analysis, achieving high color accuracy with reduced processing time.
Solution Approach 2:
The system transforms color information from detailed spectral representations into optimized parameter spaces that can be efficiently applied to images. By precomputing and storing transformation parameters for various color styles and requirements, the system can quickly apply these parameters to input images without performing time-consuming color space conversions during processing.
3Manufacturing precision
If multiple transformation parameters are applied simultaneously, then the transformation quality is improved, but the model complexity increases
Solution Approach 1:
The transformation process is segmented into independent modules: style transformation module, color transformation module, and abstraction control module. Each module handles a specific aspect of transformation and can operate independently, allowing the system to achieve high transformation quality through coordinated module operation without requiring a single complex monolithic model.
Solution Approach 2:
The pretrained model serves multiple functions simultaneously: it understands style descriptions, extracts color information, determines abstraction levels, and coordinates these transformations. This multi-functional design consolidates what would otherwise require multiple separate models into a single unified system, reducing overall model complexity while maintaining high transformation quality.
Data Source
AI summary
An image transformation method includes receiving an input image, a target style and target color information; generating a texture-transformed image in which the input image is transformed to a texture corresponding to the target style and a color-transformed image in which the input image is transformed to a color corresponding to the target color information, using a pretrained transformation model; and synthesizing the texture-transformed image and the color-transformed image to generate a result image in which the input image is transformed according to the target style and the target color information.