Hannu Laamanen

Weighted compression of spectral color information

Spectral color information is used nowadays in many different applications. Accurate spectral images are usually very large files, but a proper compression method can reduce needed storage space remarkably with a minimum loss of information. In this paper we introduce a principal component analysis (PCA) -based compression method of spectral color information. In this approach spectral data is weighted with a proper weight function before forming the correlation matrix and calculating the eigenvector basis. First we give a general framework for how to use weight functions in compression of relevant color information. Then we compare the weighted compression method with the traditional PCA compression method by compressing and reconstructing the Munsell data set consisting of 1,269 reflectance spectra and the Pantone data set consisting of 922 reflectance spectra. Two different weight functions are proposed and tested. We show that weighting clearly improves retention of color information in the PCA-based compression process.

Eigenvectors of optimal color spectra

Principal component analysis (PCA) and weighted PCA were applied to spectra of optimal colors belonging to the outer surface of the object-color solid or to so-called MacAdam limits. The correlation matrix formed from this data is a circulant matrix whose biggest eigenvalue is simple and the corresponding eigenvector is constant. All other eigenvalues are double, and the eigenvectors can be expressed with trigonometric functions. Found trigonometric functions can be used as a general basis to reconstruct all possible smooth reflectance spectra. When the spectral data are weighted with an appropriate weight function, the essential part of the color information is compressed to the first three components and the shapes of the first three eigenvectors correspond to one achromatic response function and to two chromatic response functions, the latter corresponding approximately to Munsell opponent-hue directions 9YR-9B and 2BG-2R.

Number of colors generated by smooth nonfluorescent reflectance spectra

In this study, we have analyzed statistical properties of the values of the first- and second-order derivatives of spectral reflectance curves. We show that values of all four tested spectral data sets have very similar statistical properties. We set outer limits that bound the clear majority of the values of the first- and second-order derivatives. These limits define smoothness of all nonfluorescent reflectance curves, and they can be used to form a new object color solid inside classical MacAdam limits, including all possible colors generated by smooth nonfluorescent reflectance spectra. We have used the CIELAB color space and filled the new object color solid with a hexagonal closest packing-point lattice to estimate that there exist about 2.5 million different colors, when viewed under the D65 standard illumination.

Solving the inverse grating problem with the naked eye

We make use of the color sensitivity of the naked human eye to solve the inverse grating problem. We conduct color-matching experiments between simulated colors and the color of the zero diffraction order, and show that human color vision may reveal structure dimensions at an accuracy in the order of ten nanometers, which is comparable to the precision of destructive methods such as scanning electron microscopy. Our results suggest that for a wide range of structures, the color observation may help to get quick, but still accurate, results, without any sophisticated instrumentation.

Human color vision provides nanoscale accuracy in thin-film thickness characterization

We study how accurately a naked human eye can determine the thickness of thin films from the observed color. Our approach is based on a color-matching experiment between thin-film samples and a simulated color field shown on an LCD monitor. We found that the human color observation provides an extremely accurate evaluation of the film thickness, and is comparable to sophisticated instrumental methods. The remaining color differences for the matched color pairs are close to the literature value for the smallest visually perceivable color difference.

Daylight Colored Optimal Spectra for Improved Color Discrimination

In this study we introduce three daylight colored spectra, i.e. spectra with correlated color temperatures near 6500K, for improved color discrimination. This property has been estimated by the volume of the object color solid in a nearly uniform color space based on the DIN99d color difference formula. Three optimized spectra produce about 11% - 13% larger volume than the standard D65 illuminant which simulates natural daylight and improve especially the red-green color discrimination. The optimal spectra are the result of similar optimization processes, but differ in shapes, except the common gap in light power in the region 570 nm - 610 nm.

A Technique for Detecting Metameric Color Areas for the Investigation of Historical Materials

The spectral reflectance of icons is measured using a measurement system developed in our previous study, and it is applied to detect metameric color areas in the icons. In this paper, a technique for detecting metameric color areas is proposed and examined by using a test chart and ten icons painted on wooden plates. In the proposed technique, a coefficient showing the degree of metamerism is proposed; based on the definition of metamerism whereby two stimuli can match in color while having different spectral reflectance functions. The experimental results can then be used to consider which parts of the icons have previously been repainted as restoration treatments. Despite the necessity of further consideration using certain chemical analyses and so on to conclude whether or not the experimental results are reliable, they demonstrate that the proposed technique has the basic ability to detect metameric color areas.

Spectral based optimization of screen images for industrial product presentation

In this paper, we present results for optimizing images for an industrial show room. The light conditions are not very controllable and the projector is not a high quality one. The optimization is done using metameric reproduction and to do this we measure spectral information of the product, projector and the illumination at the show room. The spectral characteristic of the red channel of the projector was surprising: the range of possible red values was narrower than the green and blue range. This caused some limitations which needed to be taken into account in calculating the optimal images: optimal images can have either full contrast range with a reddish tint or correct hue with narrower contrast range.

Application of spectral information to investigate historical materials – detection of metameric color area in icon images -

The spectral reflectance of Icons is estimated from RGB digital images taken by a digital camera, and it is applied to detect metameric color areas in the Icons. In this paper, two detection methods are proposed and examined by using a test chart and ten Icons painted on wooden plates. The first method is based on the definition of metamerism that two stimuli can match in color while having a disparate spectral reflectance. The second method is based on a phenomenon that the variation of the color difference between two colors is changed by replacing the illuminant if the colors are metamers to each other. The experimental results can be used to consider which parts of the Icons have been repainted as restoration treatments. Despite the necessity of further consideration and improvement, the experimental results demonstrate that the proposed methods have the basic ability to detect metameric color areas.

Subspace approach in spectral color science

The use of wavelength spectrum to represent color in color processing has recently seen an increase in popularity in color and color image analysis. This is partly due to the need for more accurate color information, the development of spectral imaging technologies, and the availability of efficient spectral processing techniques. The need for accurate color information processing arises in a variety of industries. Using color spectrum also gives new possibilities, for example in medical diagnostics. The principal component analysis (PCA) has become a standard method in spectral color compression and spectrum reconstruction. Varieties of PCA and other similar expansions like independent component analysis (ICA) have also been studied in spectral color science. In this chapter, we give a short overview to the development of the PCA in spectral color science, introduce the use of PCA and ICA, and list some of the applications, where PCA has been utilized in the field of spectral color science.