Generating optimal look-up tables to achieve complex color space transformations

Srivastava, Satyam; Ha, Thanh H.; Delp, Edward J.; Allebach, Jan P.

doi:10.1109/icip.2009.5413721

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…Some of these results were presented in earlier publications. 41,62 In the second part, we compare the LUTbased system with a profile-based system, and here we do not use the NLXWP as a reference. We note that our original problem was to obtain a visual output on a given display device (user device) that matches the visual output on a reference device.…”

Section: Resultsmentioning

confidence: 99%

“…It was observed in our experiments that the use of squared inverse distance ͓l i = d 2 ͑s , i͔͒ results in lower errors when 3D LUTs were used to approximate NLXWP than when using simple inverse distance ͓l i = d͑s , i͔͒. 41 This is summarized in Table I where the average errors of the two schemes are presented with respect to a randomly generated testing set of 1000 RGB values. The errors were obtained using the LUTEVAL block described earlier.…”

Section: Interpolationmentioning

confidence: 96%

“…40 We previously described the applicability of a 3D LUT for achieving complex transformations. 41 We also described a method for obtaining the optimal parameters for such a system. In the following sections, we provide more detail about the approach and some motivation behind the proposed scheme for the optimal sampling of the RGB space.…”

Section: Efficient Realization Using Look-up Tablesmentioning

confidence: 99%

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Color Management Using Optimal Three-Dimensional Look-Up Tables

Srivastava¹,

Delp²,

Ha³

et al. 2010

jist

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Digital imaging has seen an unprecedented growth in the past five years. The variety of imaging systems available to users to create and view visual data is enormous. Color management has become an important aspect of modern imaging and display systems. Color profiles have been the de facto tool for achieving faithful visual reproduction for a long time. In this article, the authors discuss issues associated with profile-based color management systems. The authors describe an alternative approach motivated by the problem of visually matching two known display devices. The authors use a model-based method to achieve this goal and propose realizing the method with simple table look-up operations. The authors devise a framework for designing look-up tables (LUTs) which are optimal in terms of color reproduction on the displays based on resource constraints. The LUT-based color management system is shown to be more accurate and memory-efficient than a comparable International Color Consortium profile-based system.

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Section: Resultsmentioning

confidence: 99%

Section: Interpolationmentioning

confidence: 96%

Section: Efficient Realization Using Look-up Tablesmentioning

confidence: 99%

See 1 more Smart Citation

Color Management Using Optimal Three-Dimensional Look-Up Tables

Srivastava¹,

Delp²,

Ha³

et al. 2010

jist

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“…8 by mapping the input c∈Q to its output c′∈S using discrete sample data points in Q and S [24]. An overview on the derivation of the dye amount LUT when the target staining condition is represented by a reference slide is described as follows:…”

Section: Quantification Of Dye Amountmentioning

confidence: 99%

Staining Correction in Digital Pathology by Utilizing a Dye Amount Table

Bautista

Yagi

2015

J Digit Imaging

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The stained colors of the tissue components are popularly used as features for image analysis. However, variations in the staining condition of the histology slides prompt variations to the color distribution of the stained tissue samples which could impact the accuracy of the analysis. In this paper, we present a method to correct the staining condition of a histology image. In the method, a look-up table (LUT) based on the dye amounts absorbed by the sample is built. The LUT can be built when either (i) the source and reference staining conditions are specified or (ii) when the user simply wants to recreate his/her preferred staining condition without specifying any reference slide. The effectiveness of the present method was evaluated in two aspects: (i) CIELAB color difference of nuclei, cytoplasm, and red blood cells, between the ten different slides of liver tissue, and (ii) classification of the different tissue components. Application of the present staining correction method reduced the color difference between the slides by an average factor of 9.8 and the classification performance of a linear discriminant classifier improved by 16.5 % on the average. Results of the paired t test statistical analysis further showed that the reduction in the CIELAB color difference between the slides and the improvement in the classifier's performance when staining correction was implemented is significant at p<0.001.

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