Sampling Optimization for Printer Characterization

Printer characterization usually requires a large number of printer inputs and corresponding color measurements of the printed outputs. In this work a sampling optimization for printer characterization based on 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 alpha=0.05 significance level) than sampling optimization techniques in the state of the art optimized for each individual substrate, thus allowing to use a single set of characterization samples for multiple substrates.

Source code
Available upon request to the authors.



Sampling Optimization for Printer Characterization by Direct Search
(Simone Bianco, Raimondo Schettini) In IEEE Transactions on Image Processing, volume 21, number 12, pp. 4868-4873, 2012.

 author = {Bianco, Simone and Schettini, Raimondo},
 year = {2012},
 pages = {4868-4873},
 title = {Sampling Optimization for Printer Characterization by Direct Search},
 volume = {21},
 number = {12},
 journal = {IEEE Transactions on Image Processing},
 pdf = {/download/bianco2012sampling-optimization.pdf},
 doi = {10.1109/TIP.2012.2211029},
 projectref = {}}