Color Conversion Lookup Table Correction for Image Forming Apparatus
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Solution Overview
Problem
Existing image forming technologies require cumbersome parameter management and increased memory usage for color tone matching, and fail to correct RGB values accurately across different image readers, leading to potential overcorrection issues.
Innovation Solution
An image forming apparatus and method that includes a scanner, memory for storing a color conversion lookup table, and circuitry to correct and re-correct the table based on pixel number and hue change, using read information from model and output products, thereby simplifying color tone adjustment without requiring feature value extraction and parameter calculation for each RGB image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If feature value extraction and parameter calculation are performed for each RGB image to achieve color tone matching, then color conversion accuracy is improved, but device complexity and memory requirements increase
Solution Approach 1:
The patent segments the color conversion process into two distinct phases: a learning phase where the scanner reads a learning chart to establish baseline color characteristics, and a correction phase where actual image conversion occurs using stored conversion parameters. This segmentation eliminates the need for complex per-image feature extraction while maintaining color accuracy.
Solution Approach 2:
The system performs preliminary color calibration by scanning a learning chart before actual image conversion. The scanner reads the learning chart to determine scanner-specific color characteristics in advance, storing these as conversion parameters that are then applied to subsequent images without requiring repeated complex analysis.
2Measurement precision
If feature value extraction and parameter calculation are performed for each RGB image to achieve color tone matching, then color conversion accuracy is improved, but memory area increases
Solution Approach 1:
The patent extracts only the essential color conversion parameters from the learning chart scan results, storing only the necessary correction values in memory rather than retaining complete per-image feature data. This extraction approach maintains color conversion accuracy while significantly reducing memory requirements.
Solution Approach 2:
The system transforms the complex per-image feature values into simplified conversion parameters through the learning phase. These changed parameters represent color correction data that can be stored compactly and applied efficiently to multiple images without requiring large memory capacity.
3Measurement precision
If color conversion lookup table is corrected based on single image data, then color tone matching is achieved, but overcorrection occurs when using different image readers
Solution Approach 1:
The learning chart serves as an intermediary standard object that mediates between different scanners and the color conversion process. Each scanner reads the same learning chart to establish its own baseline characteristics, creating a common reference point that ensures consistent and reliable color conversion across multiple readers without overcorrection.
Solution Approach 2:
The system implements feedback by comparing the scanner's reading of the learning chart against known reference values, then using this feedback to adjust and store appropriate conversion parameters. This feedback mechanism ensures that each scanner calibrates itself correctly, achieving reliable color tone matching consistent across different readers.
Data Source
AI summary
An image forming apparatus includes: a scanner to read each of a first output product serving as a model and a second output product output from the image forming apparatus; a memory that stores a color conversion lookup table to be used when color conversion is performed from a RGB color system into a CMYK color system; and circuitry to correct the color conversion lookup table based on a number of pixels and an amount of change per hue, using read information on the first output product, and re-correct the corrected color conversion lookup table, using read information on the second output product.