Halftone Processing Reordering via Threshold Array Segmentation
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Solution Overview
Problem
Multi-level halftoning techniques are computationally expensive due to the high complexity of processing large threshold arrays, leading to inefficiencies in image reproduction, especially in printers with limited cache capacity.
Innovation Solution
A halftone system that partitions the source image into segments and the threshold array into segments, allowing for out-of-order processing of image segments based on mutual threshold segments, minimizing cache misses by fetching and processing smaller threshold segments instead of the entire array.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multi-level halftoning is used to improve image reproduction fidelity, then image quality is improved, but computational complexity increases
Solution Approach 1:
The patent divides the source image into multiple image segments and the threshold array into multiple threshold segments. By processing segments independently and in parallel where possible, the computational complexity is reduced while maintaining the quality benefits of multi-level halftoning. Each segment can be processed with its corresponding threshold segment, avoiding the need to load and process the entire large threshold array at once.
2Reliability
If the entire threshold array is processed at once to ensure complete image processing, then processing completeness is improved, but cache memory usage increases
Solution Approach 1:
The threshold array is divided into multiple smaller threshold segments that can be loaded into cache memory individually. Each threshold segment corresponds to specific image segments, allowing the processor to work with smaller data subsets that fit within cache memory constraints, thereby reducing overall cache memory usage while maintaining processing completeness through systematic segment-by-segment processing.
Solution Approach 2:
The patent performs preliminary partitioning of both the source image and threshold array into segments before processing. This preliminary organization allows the system to efficiently match image segments with their corresponding threshold segments, ensuring that all necessary data is prepared and accessible before actual halftoning operations begin, thus maintaining processing completeness without requiring the entire threshold array to be in cache simultaneously.
3Measurement precision
If sequential processing of image segments is used to maintain processing order, then processing accuracy is improved, but processing speed decreases
Solution Approach 1:
By segmenting the image and threshold array, the patent enables independent processing of each segment group. Within each segment group, segments that reference the same threshold segment can be processed together, allowing for parallel processing opportunities while maintaining the necessary processing order through the segment grouping structure. This segmentation approach balances accuracy requirements with speed improvements.
Solution Approach 2:
The patent merges multiple image segments into segment groups based on their shared threshold segment references. By combining segments that can be processed together using the same threshold data, the system reduces the total number of processing passes required and enables parallel processing of segment groups, thereby improving processing speed while maintaining accuracy through the structured grouping approach.
Data Source
AI summary
Systems, methods, and software for halftoning. In an embodiment, a halftone system is configured to store a threshold array comprising an array of threshold elements. The halftone system is further configured to receive a source image comprising an array of pixels with pixel values, and perform a multi-level halftoning process on the source image by partitioning the source image into a sequence of image segments, partitioning the threshold array into a plurality of threshold segments, identifying image segment groups each comprising a set of the image segments that are noncontiguous in the sequence and correspond with a mutual threshold segment of the threshold array, and schedule processing of the image segments in an image segment group as a batch based on the mutual threshold segment.