Computational Halftoning via Dot Function Averaging
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Solution Overview
Problem
Existing digital halftoning methods in print engines are memory-intensive and limited to specific screen angles and frequencies, making them unsuitable for real-time application in print engines with limited memory, and they are not adaptable to input digital image data of varying bit depths, leading to aliasing and quantization artifacts.
Innovation Solution
A computational halftoning process that defines a halftone dot function to compute halftoned pixel values based on dot coordinates, transforms high-resolution printer coordinates into dot coordinates, and averages these values to determine halftoned pixel values at printer resolution, allowing for arbitrary screen angles and frequencies without the need for large memory look-up tables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If look-up tables defining halftone dot shape are used, then halftoning can be performed, but memory requirements become excessively high
Solution Approach 1:
The patent extracts the halftone dot shape definition from large memory-intensive look-up tables and implements it through a computational function that calculates dot positions and shapes on-the-fly based on screen angle and frequency parameters, eliminating the need for storing extensive pre-computed halftone data in memory
Solution Approach 2:
The patent changes the approach from storing fixed halftone patterns to dynamically computing them using adjustable parameters (screen angle, screen frequency), allowing the same memory resources to support multiple halftoning configurations without requiring separate look-up tables for each configuration
2Device complexity
If fixed look-up tables are used for halftoning, then processing can be simplified, but adaptability to different screen angles and frequencies is lost
Solution Approach 1:
The patent creates a universal halftoning function that can handle multiple screen angles and frequencies through parameter adjustment rather than requiring separate specialized look-up tables for each configuration, making the halftoning engine adaptable to different printing requirements while maintaining a unified processing framework
Solution Approach 2:
The patent transitions from static pre-defined halftone patterns in look-up tables to dynamic computational generation of halftone patterns based on adjustable parameters, enabling real-time adaptation to different screen angles and frequencies without reconfiguring the underlying processing architecture
3Productivity
If conventional halftoning methods are used, then processing speed may be maintained, but aliasing artifacts appear in the output
Solution Approach 1:
The patent addresses aliasing by incorporating high-resolution coordinate transformation that operates in an enhanced dimensional space, computing halftone dot positions with greater precision before mapping them to the final printer resolution, thereby eliminating aliasing artifacts while maintaining processing efficiency through optimized coordinate math
4Ease of manufacture
If look-up tables are used for halftoning, then implementation can be straightforward, but they are limited to specific screen angles and frequencies
Solution Approach 1:
The patent replaces fixed look-up tables with a parameter-driven computational model where screen angle and frequency are adjustable inputs to the halftone function, allowing the same implementation to adapt to different halftoning requirements without requiring separate pre-computed tables for each configuration
Data Source
AI summary
A computational halftoning process determines a halftoned image having halftoned pixel values by processing an array of input pixels. For each input pixel, an array of high-resolution printer coordinates is defined at a higher spatial resolution than the printer resolution, and a coordinate transformation is applied to determine a corresponding array of high-resolution dot coordinates. An array of high-resolution halftoned pixel values is then determined responsive to the code value of the input pixel by addressing a halftone dot function using the array of high-resolution dot coordinates. A halftoned pixel value is then determined by averaging the high-resolution halftoned pixel values.