Color Management of Printers by Regression over Enclosing Neighborhoods

Abstract

A popular color management standard for controlling color reproduction is the ICC color profile. The core of the ICC profile is a look-up-table which maps a regular grid of deviceindependent colors to the printer colorspace. To estimate the look-up-table from sample input-output colors, local linear regression has been shown to work better than other methods. An open problem in local linear regression is how to define the locality or neighborhood for each of the local linear regressions. In this paper, new adaptive neighborhood definitions and regularized local linear regression are proposed to address this problem. The adaptive neighborhood definitions enclose the test sample, and are motivated by a result showing they yield bounded estimation variance. An experiment shows that both regularization and the proposed neighborhoods can lead to a significant reduction in error.

Efficient Computation of Voronoi Neighbors Based on Polytope Search in Pattern Recognition

“…The k parameter in k-NN is usually chosen by means of a cross-validation process over the training samples (Duda et al, 2001). Instead of using a fix k value for the whole dataset, it will be useful to define a neighborhood that locally adapts to the data without the need for crossvalidation (Gupta et al, 2008;Chin et al, 2007). The natural neighbors for a test point q can be defined from the Voronoi tessellation of the training set as the set of training instances p i whose Voronoi cell contains (or are adjacent to the cell containing) q.…”