Context-Aware Color Reduction via Image Segmentation
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Solution Overview
Problem
Existing color reduction algorithms lead to a loss in visual acuity of images due to their reliance on entire image interpolation and manual selection methods being costly and time-consuming, often prioritizing background colors over foreground objects.
Innovation Solution
An automated method for color reduction based on image segmentation, which segments images into regions, assigns weights based on relevance, prominence, focus, and position, and selects a color palette to preserve visual acuity by prioritizing important image components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If typical color reduction algorithms are used, then the number of colors is reduced, but visual acuity is lost
Solution Approach 1:
The image is divided into multiple regions based on color similarity and spatial proximity. Each region is processed independently to identify dominant colors, allowing the algorithm to preserve important visual information while reducing the overall color count. This segmentation approach prevents the loss of visual acuity that occurs with global color reduction methods.
Solution Approach 2:
Different regions of the image are treated differently based on their local characteristics. The algorithm identifies and preserves dominant colors in each region while allowing more aggressive reduction in less important areas. This local quality approach ensures that visually important regions maintain their color fidelity while still achieving overall color reduction.
2Manufacturing precision
If manual color reduction is performed, then visual acuity is preserved, but the process is costly and time consuming
Solution Approach 1:
The system performs automated color reduction by having the image itself guide the process. The algorithm automatically identifies dominant colors and important regions through computational analysis of color distributions and spatial relationships, eliminating the need for manual intervention while preserving visual acuity. This self-service approach achieves manual-quality results automatically.
Solution Approach 2:
The algorithm transforms the color reduction problem from a manual selection process to an automated computational process by changing parameters such as color thresholds, region sizes, and dominance criteria. These parameter adjustments enable the system to automatically identify and preserve important colors while reducing the overall color count, achieving both speed and quality.
3Quantity of substance
If background colors are prioritized in color reduction, then file handling is improved, but foreground objects lose visual quality
Solution Approach 1:
The algorithm performs preliminary analysis to identify foreground objects and their dominant colors before executing the color reduction. By pre-identifying important regions and their characteristic colors, the system can prioritize these colors in the reduction process, ensuring that foreground objects maintain their visual quality even as the overall color count is reduced.
Solution Approach 2:
The system uses feedback from color distribution analysis and region importance assessment to dynamically adjust which colors are preserved during reduction. Colors from foreground regions receive higher priority weights in the selection process, while background colors are reduced more aggressively. This feedback mechanism ensures that foreground object quality is maintained while achieving effective color reduction.
Data Source
AI summary
A method, apparatus, and non-transitory computer readable medium for color reduction based on image segmentation are described. The method, apparatus, and non-transitory computer readable medium may provide for segmenting an input image into a plurality of regions, assigning a weight to each region, identifying one or more colors for each of the regions, selecting a color palette based on the one or more colors for each of the regions and the corresponding weight for each of the regions, and performing a color reduction on the input image using the selected color palette to produce a color reduced image. The weight assigned to each region may depend on factors including relevance, prominence, focus, position, or any combination thereof.