Sampling Optimization for Printer Characterization by Direct Search

• Published in: IEEE transactions on image processing 2012-12, Vol.21 (12), p.4868-4873
• Main Authors: Bianco, Simone, Schettini, R.
• Format: Article
• Language: English
• Subjects: Accuracy; Applied sciences; Color; Color characterization; Exact sciences and technology; Image processing; Information, signal and communications theory; optimization; Optimization methods; Printers; Printing; sampling; Sampling, quantization; Signal and communications theory; Signal processing; Substrates; Telecommunications and information theory
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2012.2211029

Printer characterization usually requires many printer inputs and corresponding color measurements of the printed outputs. In this brief, a sampling optimization for printer characterization on the basis of direct search is proposed to maintain high color accuracy with a reduction in the number of characterization samples required. The proposed method is able to match a given level of color accuracy requiring, on average, a characterization set cardinality which is almost one-fourth of that required by the uniform sampling, while the best method in the state of the art needs almost one-third. The number of characterization samples required can be further reduced if the proposed algorithm is coupled with a sequential optimization method that refines the sample values in the device-independent color space. The proposed sampling optimization method is extended to deal with multiple substrates simultaneously, giving statistically better colorimetric accuracy (at the α = 0.05 significance level) than sampling optimization techniques in the state of the art optimized for each individual substrate, thus allowing use of a single set of characterization samples for multiple substrates.