Segmented Color Analysis for Automatic Image Treatment Suggestions
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Solution Overview
Problem
Existing image processing systems lack the ability to automatically suggest aesthetically-pleasing image treatment options based on color analysis, particularly for images intended for use as wallpapers, photo album covers, or avatars.
Innovation Solution
A method involving image segmentation, histogram analysis, and color filtering to determine dominant hues and luminance values, followed by suggesting image treatments such as color palettes and brightness adjustments based on these analyses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual image treatment adjustment is used, then users can control image aesthetics, but it requires significant user time and effort
Solution Approach 1:
The system performs self-service by automatically analyzing image colors, determining dominant hues and luminance values, and generating treatment suggestions without requiring user intervention. The histogram analysis and color processing occur autonomously, with the system serving itself to produce aesthetic recommendations.
Solution Approach 2:
The system performs preliminary action by pre-calculating color histograms, identifying dominant colors, and determining luminance values before presenting treatment options to the user. This preliminary color analysis prepares the foundation for automated treatment suggestions, reducing the need for manual trial-and-adjustment cycles.
2Measurement precision
If comprehensive color analysis is performed, then treatment suggestions are more accurate, but processing time increases
Solution Approach 1:
The system extracts only the essential color characteristics needed for treatment suggestions by identifying dominant hues and luminance values from the color histogram. Rather than analyzing every pixel parameter, it extracts the key color attributes that most significantly impact aesthetic treatment decisions, reducing processing overhead while maintaining accuracy.
Solution Approach 2:
The system changes parameters by converting raw pixel data into histogram representations and then identifying dominant color parameters. This parameter transformation from individual pixel values to aggregated color statistics enables efficient analysis while preserving the essential color information needed for accurate treatment recommendations.
3Loss of information
If background segmentation is applied, then color analysis focuses on relevant areas, but computational complexity increases
Solution Approach 1:
The system applies segmentation by separating background pixels from foreground pixels using a segmentation map. This division allows the color analysis to focus specifically on background dominant hues and luminance values, excluding foreground elements that would otherwise contaminate the color histogram and treatment suggestions.
Data Source
AI summary
A method of image processing is disclosed herein, the method comprising: obtaining a first image comprising a first plurality of pixels; obtaining a first segmentation map for the first image, wherein the first segmentation map comprises a first plurality of segmentation values; performing a histogram analysis on the first plurality of pixels, wherein the histogram analysis is weighted based on each pixel's corresponding segmentation value in the first segmentation map, and wherein the histogram analysis comprises determining at least: (a) a median luminance value for pixels that are estimated (e.g., based on their segmentation values) to be part of the background of the first image; and (b) one or more dominant hues for pixels estimated to be part of the background of the first image; and suggesting a first image treatment for the first image based on the determined median luminance value and/or the determined one or more dominant hues.