Color Replacement System Using LAB Space Embeddings
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Solution Overview
Problem
Conventional image editing methods for color replacement are time-consuming and inaccurate, as they require manual pixel selection or RGB value specification, which does not align with human color perception.
Innovation Solution
A color replacement system that uses natural language inputs to identify source and target colors, generating color embeddings to segment and replace colors in images, allowing for efficient and accurate color changes while preserving shade variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual pixel selection or RGB value specification is used for color replacement, then color selection can be performed, but the process becomes time-consuming and inaccurate
Solution Approach 1:
The patent replaces manual mechanical pixel selection with an automated image processing system that uses color space transformation and clustering algorithms. The system automatically identifies and segments target colors through computational methods, eliminating the need for manual pixel-by-pixel selection while improving both accuracy and efficiency.
Solution Approach 2:
The patent transforms color selection from RGB space to LAB color space, changing the parameter representation to better align with human color perception. This parameter transformation enables more accurate color segmentation by using perceptually uniform color differences, resolving the accuracy issue while maintaining automation.
2Measurement precision
If RGB values are used to select colors, then color selection can be performed, but the selection is inaccurate because RGB distance does not correspond to human color perception
Solution Approach 1:
The patent converts color parameters from RGB space to LAB color space, where the L* channel represents lightness and a* and b* channels represent color opposites. This parameter change creates a perceptually uniform color space where Euclidean distance corresponds to human color perception, enabling accurate color selection without requiring users to understand complex RGB values.
Solution Approach 2:
The patent introduces LAB color space as an intermediary between RGB input and color selection output. This intermediary transformation layer bridges the gap between digital color representation and human color perception, allowing users to select colors based on perceptual similarity rather than numerical RGB distance.
3Productivity
If conventional color replacement methods are used, then color changes can be made, but manual pixel selection is required which reduces productivity
Solution Approach 1:
The patent performs preliminary color segmentation and clustering before the actual color replacement operation. By pre-identifying and segmenting target color regions using K-means clustering in LAB color space, the system prepares the image data in advance, enabling rapid color replacement without manual pixel selection during the execution phase.
Solution Approach 2:
The patent segments the image into distinct color regions using clustering algorithms based on LAB color values. This segmentation automatically groups pixels with similar colors, creating masks that define the regions to be replaced. The segmentation process eliminates the need for manual pixel selection and enables efficient batch processing of color replacements.
Data Source
AI summary
Systems and methods for color replacement are described. Embodiments of the disclosure include a color replacement system that adjusts an image based on a user-input source color and target color. For example, the source color may be replaced with the target color throughout the entire image. In some embodiments, a user provides a speech or text input that identifies a source color to be replaced. The user may then provide a speech or text input identifying the target color, replacing the source color. A color replacement system creates and embedding of the source color, segments the image based on the source color embedding, and then replaces the color of segmented portion of the image with the target color.