Joint Quantization of Drop Probability Functions in Inkjet Printers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Multi-level inkjet printers face performance degradation due to quantization errors in probability density functions, which are exacerbated by the use of separate quantization for each drop size, leading to inferior halftoning compared to bi-level systems.
Innovation Solution
Implementing a sequential method of quantizing probability density functions, where errors from quantizing larger drops are diffused to modify non-quantized functions for smaller drops, ensuring all drop size functions are jointly quantized, reducing overall quantization error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If separate quantization is applied to each probability density function for different drop sizes, then the quantization process is simple to implement, but quantization errors accumulate quickly and degrade halftoning performance
Solution Approach 1:
The patent merges the quantization process across multiple probability density functions by accumulating quantization errors from previously quantized PDFs and adding them to subsequent PDFs before quantization. This combining approach ensures that quantization errors do not accumulate independently for each drop size, thereby improving halftoning accuracy while maintaining implementation simplicity.
Solution Approach 2:
The patent implements feedback by taking the quantization error from each quantized PDF and feeding it back into the next PDF to be quantized. This feedback mechanism allows subsequent PDFs to compensate for previous quantization errors, preventing error accumulation and maintaining high halftoning quality across all drop sizes.
2Adaptability or versatility
If more drop sizes are supported in multi-level inkjet printers, then printing quality and flexibility are improved, but quantization errors accrue more quickly and degrade performance
Solution Approach 1:
The patent applies feedback by accumulating quantization errors from each additional drop size PDF and incorporating them into subsequent PDFs. This feedback mechanism ensures that as more drop sizes are added to enhance versatility, the quantization errors do not compound, thereby maintaining reliable and stable halftoning performance across all supported drop sizes.
Solution Approach 2:
The patent ensures continuity by maintaining a running accumulation of quantization errors that is continuously applied to each subsequent PDF. This continuous error compensation mechanism ensures that the addition of more drop sizes does not interrupt the quality of halftoning, preserving performance stability across the full range of supported drop sizes.
3Productivity
If standard rounding quantization is used for probability density functions, then the quantization process is computationally efficient, but mass errors increase and probability distribution validity may be compromised
Solution Approach 1:
The patent maintains computational efficiency by using simple rounding operations but adds a feedback step where quantization errors are accumulated and applied to subsequent PDFs. This feedback mechanism corrects mass errors without requiring complex computational processes, thereby preserving both processing speed and accuracy.
Solution Approach 2:
The patent introduces an intermediary error accumulation variable that mediates between the simple rounding operation and the final PDF values. This intermediary stores the quantization errors and distributes them to subsequent PDFs, acting as a buffer that maintains both computational efficiency and mass error accuracy.
Data Source
AI summary
Methods and systems for sequentially quantizing probability density functions for inkjet printing. In an example embodiment, an operation can be implemented to quantize probability density functions associated with larger drops among a plurality of drops provided by an inkjet printer. Non-quantized probability functions associated with remaining ink drops are then modified utilizing an error incurred at each quantization during quantization of the probability density functions associated with the larger drops. Quantizing the probability density functions and modifying the non-quantized probability functions continue until all drop size probability functions associated with the plurality of drops are quantized.