Digital Image Segmentation for Adaptive Ink Reduction
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Solution Overview
Problem
Current printing technologies lack efficient methods to reduce ink demand in real-time for printing jobs, requiring manual user input and resulting in high resource consumption and costs, especially in commercial settings.
Innovation Solution
A method that uses image processing and machine learning techniques to segment digital images, analyze properties of image segments, and alter them to reduce ink demand by targeting non-informative areas, thereby minimizing ink usage while maintaining important information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If manual methods (reducing resolution, greyscale printing) are used to reduce ink demand, then ink consumption decreases, but user convenience deteriorates and real-time adaptation is lost
Solution Approach 1:
The system performs automatic image analysis and modification without requiring user intervention. The processor autonomously identifies informative versus non-informative segments, applies appropriate modifications, and generates the final print-ready image, allowing the system to serve itself in optimizing ink usage while maintaining quality where needed.
Solution Approach 2:
The system dynamically changes image parameters (resolution, color depth, compression) on a segment-by-segment basis. Informative segments maintain high quality parameters while non-informative segments undergo aggressive parameter reduction, enabling adaptive ink savings without uniform quality degradation.
2Loss of substance
If uniform processing is applied to the entire image, then processing simplicity is maintained, but ink savings are suboptimal due to inability to differentiate informative from non-informative areas
Solution Approach 1:
The image is divided into multiple segments or regions, each independently analyzed for informativeness. This segmentation allows the system to apply different processing strategies to different areas, identifying and modifying non-informative segments while preserving informative ones, thereby achieving targeted ink reduction without uniformly degrading image quality.
Solution Approach 2:
Different quality levels and processing intensities are applied to different regions of the image based on their informational value. Critical areas maintain high fidelity while peripheral or redundant areas receive aggressive optimization, creating a locally adaptive quality distribution that maximizes ink savings where permissible.
3Loss of substance
If real-time adaptive processing is implemented, then ink demand is optimized per print job, but processing time increases
Solution Approach 1:
The system performs image analysis and modification operations before the actual printing process begins. By pre-processing the image to identify and modify non-informative segments, the system prepares an optimized version ready for immediate printing, eliminating the need for real-time adjustments during the printing operation itself.
Data Source
AI summary
A method, apparatus, and non-transitory computer-readable storage medium for altering a digital image for a printing job, the method comprising receiving a requested printing job including the digital image, performing a segmentation on the digital image, extracting values of properties for a segment of the segmented digital image, determining, based on the extracted values of the properties for the segment, whether the segment of the digital image should be altered, and altering the segment of the digital image when it is determined the segment of the digital image should be altered, a resulting altered digital image being transmitted to a printer for printing.