Subtractive Color Processing for Image Quality and Data Size
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional subtractive color processing methods are inefficient for images with many areas intended to be represented by a single color, such as graphs and graphics, leading to image quality degradation and increased processing time.
Innovation Solution
A method that identifies the most used color in an image and extracts small pixel groups with minimal color difference to this dominant color, replacing areas with significant color differences to generate a resultant image with improved quality and reduced data size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional subtractive color processing methods are used on images with many single-color areas, then processing is performed on all areas, but image quality degrades and processing time increases
Solution Approach 1:
The image is segmented into different types of areas (single-color areas vs. multi-color areas) based on color variation analysis. This allows the processing method to be selectively applied only where necessary, rather than uniformly across the entire image, thus reducing processing time while maintaining image quality in critical areas.
Solution Approach 2:
Different processing strategies are applied to different regions of the image based on their local characteristics. Single-color areas use one approach (color substitution with representative color), while multi-color areas use another approach (conventional processing), optimizing both quality and efficiency for each region's specific needs.
2Manufacturing precision
If conventional subtractive color processing methods are used on images with many single-color areas, then all areas are processed, but data size increases without significant quality improvement
Solution Approach 1:
The image is divided into single-color areas and multi-color areas, allowing differential processing. Single-color areas undergo color substitution that reduces data complexity, while multi-color areas receive conventional processing, optimizing the balance between quality and data size for each region.
Solution Approach 2:
Processing intensity and method are adapted to local area characteristics. In single-color areas, aggressive color substitution reduces data size effectively, while in multi-color areas, more conservative processing preserves quality without unnecessarily increasing data size.
3Quantity of substance
If color reduction is performed by reducing halftone levels, then compression is achieved, but areas with similar colors are converted to identical colors, changing graph significance
Solution Approach 1:
The processing approach is adapted based on local area characteristics. In single-color areas where color uniformity is acceptable, aggressive color substitution achieves compression. In multi-color areas where color distinction is critical, conventional processing preserves color variations, maintaining graph significance while still achieving some compression through selective color substitution.
Solution Approach 2:
The color substitution threshold and representative color selection are adjusted based on local area properties. This allows flexible control over the compression-quality tradeoff, achieving better compression in areas where it matters less while preserving critical color information in areas where it matters most.
Data Source
AI summary
A most used color that is a most popularly used color is obtained in an image. Pixel groups formed of continued pixels having an identical color other than the most used color in the image is extracted as first pixel groups. Pixel groups having a color of which a color difference with respect to the most used color is smaller than a predetermined threshold and having a size thereof that is smaller than a predetermined size is extracted as third pixel groups among the first pixel groups thus extracted. The first pixel groups other than the third pixel groups are taken as second pixel groups. An image is generated by replacing colors of portions, which correspond to the second pixel groups in an image having an area identical with that of an image to be processed and filled with the most used color, with the corresponding second pixel groups, respectively.