Color Conversion Model Creation via Selective Pixel Sampling
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Solution Overview
Problem
Existing image processing systems face challenges in creating an accurate color conversion model due to insufficient or excessive color data sampling, leading to decreased accuracy and potential noise in color adjustments.
Innovation Solution
An image processing apparatus that specifies areas for color data extraction, determines optimal intervals based on the number of colors, tones, and patterns, and extracts color conversion information from corresponding pixels in both original and adjusted images to create a precise color conversion model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If color data is extracted from all pixels in the image, then the color conversion model accuracy is improved, but the processing time and computational load increase significantly
Solution Approach 1:
The image is divided into multiple regions based on color characteristics. Instead of processing all pixels uniformly, the system segments the image into regions with similar color properties and selects representative pixels from each region, significantly reducing the number of pixels to be processed while maintaining color conversion accuracy.
Solution Approach 2:
Different processing strategies are applied to different regions of the image based on their local color characteristics. Regions with complex color variations receive more detailed sampling, while uniform regions use coarser sampling, optimizing the balance between accuracy and processing efficiency.
2Productivity
If color data is extracted from fewer pixels to reduce processing time, then the processing speed is improved, but the color conversion model accuracy decreases
Solution Approach 1:
Before extracting color data, the system performs preliminary analysis to identify representative pixels and regions of interest. This preliminary action ensures that the subsequent color data extraction focuses on the most important pixels, maintaining accuracy while reducing the overall number of pixels processed.
Solution Approach 2:
The system dynamically adjusts sampling parameters such as pixel density and region boundaries based on the specific characteristics of each image. This adaptive parameter adjustment allows the system to maintain high accuracy with fewer pixels by optimizing the sampling strategy for each individual case.
3Adaptability or versatility
If color data is extracted without selective sampling, then the color conversion model covers all color variations, but noise is introduced into the color adjustments
Solution Approach 1:
The system extracts only the essential color information from representative pixels while discarding redundant and noisy data. By selectively extracting color data from carefully chosen pixels rather than all pixels, the system maintains comprehensive color variation coverage while filtering out noise that would otherwise be included in the color conversion model.
Data Source
AI summary
An image processing apparatus includes a specifying unit, an extraction unit, and a creation unit. The specifying unit specifies, for one image among a first image before color conversion and a second image after color conversion, an area for which image information is extracted. The extraction unit extracts plural pieces of color conversion information, which are image information about pixels in the area of the one image among the first image and the second image, the area being specified by the specifying unit, and image information about pixels in the other image corresponding to the pixels in the one image. The creation unit creates a color conversion property on the basis of the plural pieces of color conversion information extracted by the extraction unit.