Subspace-Clustering-Based Multispectral Image Compression

This paper describes a subspace clustering strategy for the spectral compression of multispectral images. Unlike standard PCA, this approach finds clusters in different subspaces of different dimension. Consequently, instead of representing all spectra in a single low-dimensional subspace of a fixed dimension, spectral data are assigned to multiple subspaces having a range of dimensions from one to eight. For a given compression ratio, this tradeoff reduces the maximum reconstruction error dramatically. In the case of compressing multispectral images, this initial compression step is followed by lossless JPEG2000 compression in order to remove the spatial redundancy in the data as well

Gaussian Illuminants and Reflectances for Colour Signal Prediction

An alternative to the von Kries scaling underlying the chromatic adaptation transforms found in colour appearance models such as CIECAM02 is suggested for predicting what the colour signal (e.g., XYZ) reflected from a surface under a first illuminant is likely to become when lit instead by a second illuminant. The proposed method, G2M, employs metameric Gaussian-like functions to model the illuminant and reflectance spectra. The method’s prediction is based on relighting the Gaussian-like reflectance spectrum with the second Gaussian-like illuminant. Tests show that the proposed G2M method significantly outperforms CIECAT02

Color Calibration via Natural Food Colors

Color image calibration is usually done with the aid of a color chart such as the Macbeth ColorChecker containing a set of carefully produced color patches. However, in many consumer applications such as Internet shopping, for which the correct reproduction of color can be very important, most users will not have a color chart readily available, and probably are not interested in purchasing one in any case. We propose using the colors of the fleshy interior parts of oranges, lemons and limes, along with cooked egg white as a means of creating a simple color ‘chart’. A sample of oranges, lemons and limes from North America and Australia has shown their color to be quite consistent, and therefore potentially suitable as a set of reference colors for color image calibration. Figure 1 shows one of the images used in measuring the colors of the fruits and vegetables. In the case of Internet sales, a seller photographing color-sensitive merchandise, such as clothing, could simply include one or two of these foods in each picture. This would provide an immediate point of reference for the purchaser as to whether or not the image colors are correct. Clearly, if the food colors do not look right, neither will the merchandise when it is delivered

Dichromatic Illumination Estimation via Hough Transforms in 3D

A new illumination-estimation method is proposed based on the dichromatic reflection model combined with Hough transform processing. Other researchers have shown that using the dichromatic reflection model under the assumption of neutral interface reflection, the color of the illuminating light can be estimated by intersecting the dichromatic planes created by two or more differently coloured regions. Our proposed method employs two Hough transforms in sequence in RGB space. The first Hough Transform creates a dichromatic plane histogram representing the number of pixels belonging to dichromatic planes created by differently coloured scene regions. The second Hough Transform creates an illumination axis histogram representing the total number of pixels satisfying the dichromatic model for each posited illumination axis. This method overcomes limitations of previous approaches that include requirements such as: that the number of distinct surfaces be known in advance, that the image be presegmented into regions of uniform colour, and that the image contain distinct specularities. Many of these methods rely on the assumption that there are sufficiently large, connected regions of a single, highly specular material in the scene. Comparing the performance of the proposed approach with previous non-training methods on a set of real images, the proposed method yields better results while requiring no prior knowledge of the image content

Multispectral Color Constancy: Real Image Tests

Experiments using real images are conducted on a variety of color constancy algorithms (Chromagenic, Greyworld, Max RGB, and a Maloney-Wandell extension called Subspace Testing) in order to determine whether or not extending the number of channels from 3 to 6 to 9 would enhance the accuracy with which they estimate the scene illuminant color. To create the 6 and 9 channel images, filters where placed over a standard 3-channel color camera. Although some improvement is found with 6 channels, the results indicate that essentially the extra channels do not help as much as might be expected

Robust Chroma and Lightness Descriptors

New descriptors for lightness and chroma are presented that are based on properties of a wraparound Gaussian metameric to the given XYZ tristimulus coordinates. For the 1600 samples of the Munsell glossy set, both descriptors are found to correlate to Munsell value and chroma at least as well as the corresponding CIECAM02 descriptors when the Munsell samples are under the CIE C illuminant. However, when the illuminant is changed the new descriptors were found to be considerably more consistent under the second illuminant than those of CIECAM02

Comparing Colour Camera Sensors Using Metamer Mismatch Indices

A new method of evaluating the colorimetric accuracy of a color camera is proposed that is based on the size (appropriately normalized) of the metamer mismatch volume induced by a change of \u27observer\u27 from camera to human eye and vice-versa. The degree of metamer mismatching indicates the range in the discrepancy of the colour signals that can arise and as such is a more well-founded measure of colorimetric accuracy than traditional spectral-based measures such as the root mean squared difference in fit between the camera and eye\u27s sensitivity functions

Gaussian-Based Hue Descriptors

A robust and accurate hue descriptor that is useful in modeling human color perception and for computer vision applications is explored. The hue descriptor is based on the peak wavelength of a Gaussian-like function (called a wraparound Gaussian) and is shown to correlate as well as CIECAM02 hue to the hue designators of papers from the Munsell and Natural Color System color atlases and to the hue names found in Moroney’s Color Thesaurus. The new hue descriptor is also shown to be significantly more stable under a variety of illuminants than CIECAM02. The use of wraparound Gaussians as a hue model is similar in spirit to the use of subtractive Gaussians proposed by Mizokami et al., but overcomes many of their limitations

Simplifying Irradiance Independent Color Calibration

An important component of camera calibration is to derive a mapping of a camera’s output RGB to a deviceindependent color space such as the CIE XYZ or sRGB6. Commonly, the calibration process is performed by photographing a color chart in a scene under controlled lighting and finding a linear transformation M that maps the chart’s colors from linear camera RGB to XYZ. When the XYZ values corresponding to the color chart’s patches are measured under a reference illumination, it is often assumed that the illumination across the chart is uniform when it is photographed. This simplifying assumption, however, often is violated even in such relatively controlled environments as a light booth, and it can lead to inaccuracies in the calibration. The problem of color calibration under non-uniform lighting was investigated by Funt and Bastani2,3. Their method, however, uses a numerical optimizer, which can be complex to implement on some devices and has a relatively high computational cost. Here, we present an irradiance-independent camera color calibration scheme based on least-squares regression on the unitsphere that can be implemented easily, computed quickly, and performs comparably to the previously suggested techniqu

A Robust Hue Descriptor

A hue descriptor based on Logvinenko’s illuminantinvariant object colour atlas [1] is tested in terms of how well it maps hues to the hue names found in Moroney’s Colour Thesaurus [2] [3] and how well it maps hues of Munsell papers to their corresponding Munsell hue designator. Called the KSM hue descriptor, it correlates hue with the central wavelength of a Gaussian-shaped reflectance function. An important feature of this representation is that the set of hue descriptors inherits the illuminate invariant property of Logvinenko’s object colour atlas. Despite the illuminant invariance of the atlas and the hue descriptors, metamer mismatching means that colour stimulus shift [4] can occur, which will inevitably lead to some hue shifts. However, tests show that KSM hue is robust in the sense that it is much more stable under a change of illuminant than CIELAB hue