Multi-pass Inkjet Dot Overlap Control via Data Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing multi-pass recording systems for inkjet recording apparatuses face challenges in controlling dot coverage variations due to shifts in dot recording positions, leading to density fluctuations and granularity deterioration, as the method of uncorrelated quantization of multi-valued data fails to adequately manage overlapping dots.
Innovation Solution
An image processing apparatus that divides multi-valued image data into separate data sets for each scanning pass and common data sets, followed by quantization and combination of these data sets to control the amount of overlapping dots, ensuring low granularity and reduced density variations by performing relative scanning multiple times with a recording head and medium.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If uncorrelated quantization is applied to multi-valued data for different recording scannings, then the complexity of processing is reduced, but the control precision over overlapping dot amount deteriorates
Solution Approach 1:
The patent segments multi-valued image data into multiple sets, where each set corresponds to a specific recording scanning. This segmentation allows independent quantization processing for each scanning while maintaining the ability to control overlapping dots by managing the relationship between data sets. The segmentation approach reduces processing complexity by enabling parallel or sequential independent quantization while preserving precision through structured data organization.
Solution Approach 2:
The patent changes the parameter organization of image data by introducing scanning-specific multi-valued data sets with different parameter assignments. Each data set contains parameters tailored to its corresponding scanning operation, allowing precise control over dot placement and overlap. This parameter transformation enables both simplified processing through specialized data structures and precise control through targeted parameter management.
2Reliability
If the number of overlapping dots is increased to suppress density variations, then the suppression effect on density fluctuations improves, but the granularity of the image deteriorates
Solution Approach 1:
The patent applies local quality by differentiating the treatment of overlapping dots based on their spatial distribution and function. Different regions of the image data are assigned different multi-valued data sets with optimized characteristics for their specific requirements. This allows density variations to be suppressed in critical areas while maintaining fine granularity in areas where it is more important, achieving a localized balance between the two competing requirements.
Solution Approach 2:
The patent employs partial action by applying overlapping dot strategies selectively rather than uniformly across the entire image. By using multiple multi-valued data sets, the system can apply excessive dot overlap only where density suppression is critical, while using minimal or no overlap in areas where granularity is more important. This selective application optimizes the trade-off between density uniformity and image fineness.
3Manufacturing precision
If the number of overlapping dots is decreased to maintain image granularity, then the image quality is improved, but the suppression effect on density variations becomes insufficient
Solution Approach 1:
The patent uses local quality to apply different overlapping strategies to different regions of the image. By organizing data into multiple scanning-specific multi-valued sets, the system can maintain low overlap (preserving granularity) in most areas while concentrating overlap in specific regions where density suppression is most needed. This localized differentiation allows simultaneous achievement of fine granularity and effective density control.
Solution Approach 2:
The patent introduces multi-valued data sets as intermediaries between the recording system and the final image output. These intermediate data structures enable flexible control over dot overlap by acting as a buffer layer that can be selectively adjusted. The intermediary data sets allow the system to maintain granularity while providing the necessary overlap in specific areas to suppress density variations, resolving the contradiction through intermediate processing stages.
Data Source
AI summary
Multi-valued image data corresponding to a pixel area is divided into the first scanning multi-valued data, first and second scanning common multi-valued data, and second scanning multi-valued data. A quantization processing is executed on each of the multi-valued data to generate first scanning quantized data, first and second scanning common quantized data, and second scanning quantized data. After that, these pieces of quantized data are combined for each scanning to generate first scanning combined quantized data and second scanning combined quantized data. According to this, the amount of pixels where dots are both recorded by performing a scanning by plural times (the amount of overlapping dots) is controlled, and while suppressing the image density variations, the granularity is held to a low level.