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Updated: May 19, 2026

Quantitative Characterization of Liquid Photosensitive Bioink Properties for Continuous Digital Light Processing Based Printing

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Published on: April 14, 2023

Sampling optimization for printer characterization by direct search.

Simone Bianco¹, Raimondo Schettini

¹Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Milan, 20126, Italy. simone.bianco@disco.unimib.it

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|August 8, 2012

Summary

This summary is machine-generated.

This study introduces a new sampling optimization for printer characterization, significantly reducing the number of samples needed while maintaining color accuracy. The method also improves performance for multiple substrates.

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Area of Science:

Color Science
Digital Printing Technology
Computational Imaging

Background:

Printer characterization traditionally demands extensive input data and color measurements.
Existing methods often require a large number of samples, impacting efficiency.

Purpose of the Study:

To propose a sampling optimization method for printer characterization.
To reduce the number of required characterization samples while preserving color accuracy.
To extend the method for simultaneous multi-substrate characterization.

Main Methods:

Utilizing a direct search-based sampling optimization approach.
Comparing the proposed method against uniform sampling and state-of-the-art techniques.
Integrating sequential optimization for further refinement in device-independent color space.
Extending the method for simultaneous optimization across multiple substrates.

Main Results:

The proposed method requires, on average, one-fourth the samples of uniform sampling for comparable color accuracy.
It outperforms the best existing methods, needing only one-third of their samples.
Coupling with sequential optimization further reduces sample requirements.
Simultaneous multi-substrate optimization yields statistically superior colorimetric accuracy compared to individual substrate optimization.

Conclusions:

The developed sampling optimization significantly enhances the efficiency of printer characterization.
The method offers superior performance and reduced sample requirements for both single and multiple substrates.
This approach enables a single set of characterization samples for diverse printing substrates.