Image Color Characteristic Determination via Luminance Segmentation
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Solution Overview
Problem
Existing image processing methods struggle to accurately determine the color characteristics of images, leading to incorrect classification as neutral or color, which results in image quality defects and billing discrepancies.
Innovation Solution
A method that identifies regions in an image based on luminance values and categorizes pixels into predefined color categories using chroma values, followed by determining color characteristics through a weighted sum of pixel counts, allowing for accurate classification as color or neutral.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing methods are used to determine color characteristics, then the processing is simple and fast, but the accuracy of color determination is poor leading to incorrect classification
Solution Approach 1:
The image is divided into multiple regions based on luminance values (e.g., shadow, midtone, highlight regions). Each region is processed separately to determine color characteristics, allowing for more accurate local analysis while maintaining manageable complexity through systematic division of the processing task.
Solution Approach 2:
Different color determination criteria and thresholds are applied to different luminance regions. For example, shadow regions use different chroma thresholds than highlight regions, recognizing that color perception and characteristics vary across different brightness levels. This local adaptation improves overall accuracy without requiring complete redesign of the entire processing system.
2Reliability
If simple pixel counting methods are used, then the processing is fast and simple, but billing errors occur due to incorrect color classification
Solution Approach 1:
The image is pre-processed by dividing it into luminance-based regions before color analysis. This preliminary segmentation organizes the data structure, making subsequent color determination more efficient and accurate. The pre-organization of pixels by luminance region enables faster processing while improving billing accuracy through region-specific analysis.
Solution Approach 2:
Different chroma thresholds and color determination parameters are applied based on luminance region. Instead of using a single threshold for the entire image, the system adjusts parameters locally according to brightness levels, improving accuracy while maintaining processing efficiency through systematic parameter management.
3Manufacturing precision
If uniform color thresholds are applied to the entire image, then the processing is simple, but image quality defects occur due to incorrect neutral or color classification
Solution Approach 1:
The image is segmented into distinct luminance regions (shadow, midtone, highlight) that are analyzed separately for color characteristics. This segmentation allows for region-appropriate quality assessment, improving overall image quality by addressing the specific needs of each luminance zone rather than applying uniform criteria.
Solution Approach 2:
Each luminance region has its own color determination criteria and thresholds optimized for that specific brightness level. Shadow regions, midtone regions, and highlight regions each receive tailored analysis, ensuring that color quality is accurately assessed where it matters most for each region's characteristics.
Data Source
AI summary
Disclosed embodiments relate to a method implementable on a computing device for determining color characteristics of an image. The method includes identifying one or more regions in the image based on a luminance value associated with pixels in the image. Each region comprises a set of pixels having a predefined range of luminance values. The set of pixels associated with the one or more regions are categorized into one or more predefined color categories corresponding to each of the one or more regions, based on a chroma value associated with each pixel in the set of pixels. The color characteristics of the image are determined based on a count of the categorized pixels in each of the one or more predefined color categories.