Print Control Device Error Diffusion Random Number Calibration
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Solution Overview
Problem
Conventional inkjet printing technologies face challenges in optimizing the proportions of dot types to improve image quality, leading to regularity and quality issues in printed images due to limited dot types and potential irregularities in dot formation.
Innovation Solution
A print control device executes an error diffusion process using a processor and memory with a computer program that acquires image data, applies an error diffusion process with random number calibration to set dot values based on gradation values, and adjusts dot sizes and error values, allowing for the output of print data that optimizes dot formation across various gradation ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional error diffusion process is used with fixed threshold values, then the processing is simple and fast, but the image quality shows regularity patterns and fluctuations
Solution Approach 1:
The patent applies dynamics by making the threshold values variable rather than fixed. The threshold values are dynamically adjusted based on random numbers that are generated according to specific probability distributions. This dynamic adjustment eliminates the regularity patterns caused by fixed thresholds while maintaining processing efficiency through algorithmic generation of the random values.
Solution Approach 2:
The patent changes the parameter of threshold values from constant to variable. By introducing random numbers with specific statistical properties (mean equal to the original threshold, controlled variance), the system transforms the static threshold into a dynamic parameter that adapts locally, reducing periodic artifacts while preserving the overall error diffusion functionality.
2Manufacturing precision
If multiple dot types are used to improve image quality, then the dot formation can be optimized, but regularity issues and quality fluctuations occur
Solution Approach 1:
The patent applies asymmetry by introducing random variations in the threshold values assigned to different pixels. Instead of symmetric, uniform thresholding, each pixel receives a slightly different threshold based on random number generation. This asymmetric approach breaks the regularity patterns that cause quality fluctuations while still maintaining controlled dot formation through the statistical properties of the random values.
Solution Approach 2:
The threshold values become dynamic and adapt to local requirements rather than being static and uniform. The random number generation process creates locally adapted thresholds that respond to the specific needs of each pixel position, eliminating the periodic regularity issues while maintaining overall image quality through the controlled statistical distribution of these dynamic thresholds.
3Manufacturing precision
If random number calibration is applied to reduce regularity, then image quality improves, but the processing complexity increases
Solution Approach 1:
The patent replaces complex mechanical or computational image processing systems with a streamlined random number calibration approach. Instead of using elaborate multi-step processing algorithms, the system uses efficiently generated random numbers with specific statistical properties to achieve the same quality improvement, significantly reducing processing time while maintaining the benefits of reduced regularity patterns.
Solution Approach 2:
The patent efficiently changes the threshold parameter using random number calibration with controlled statistical properties. By generating random numbers with mean equal to the original threshold and controlled variance, the system achieves quality improvement through simple parameter adjustment rather than complex processing, minimizing the time loss associated with the calibration process.
Data Source
AI summary
In an error diffusion process, a random number acquiring unit acquires a random number included in a first random number range that depends on the gradation value of the target pixel data, in a case that the gradation value of the target pixel data is in a first range. The first correcting unit corrects the gradation value of the target pixel data into a first corrected gradation value by using the random number. The dot value setting unit sets a dot value of the target pixel data to either a first dot value or a second dot value. The first random number range corresponding to the gradation value smaller than the second threshold value includes a specific random number such that the first correcting unit corrects the gradation value into the first corrected gradation value greater than the second threshold value by using the specific random number.