Spectral Reflectivity Recovery from Tristimulus Values Using 3D Extrapolation with 3D Interpolation

A color appearance model (CAM) is an advanced colorimetric tool used to predict color appearance under a wide variety of viewing conditions. A chromatic adaptation transform (CAT) is an integral part of a CAM. Its role is to predict "corresponding colors," that is, a pair of colors that have the same color appearance when viewed under different illuminants, after partial or full adaptation to each illuminant. Modern CATs can sometimes generate colors with negative tristimulus values. For some imaging applications, it is important to maintain positive tristimulus values when applying a CAT. This article proposes a new CAT that does not operate on the standard von Kries model of adaptation. Instead, it uses a spectral reconstruction technique as an intermediate stage in the process, while still requiring only tristimulus values as inputs.It is demonstrated that the proposed CAT will not generate colors outside the spectral locus or colors with negative tristimulus values. The proposed CAT does not use established empirical corresponding-colors data sets to optimize performance, as most modern CATs do, yet it performs as well as or better than the most recent CATs when tested against the data sets of corresponding colors.

Section: Spectral Reconstruction Computationmentioning

confidence: 99%

Section: Spectral Reconstruction Computationmentioning

confidence: 99%

Chromatic adaptation transform by spectral reconstruction

Burns

2019

“…The second category used methods based on principal component analysis, abbreviated PCA . The third category performed the recovery by an interpolation technique with some known real spectra . Finally, the fourth category used several methods, such as R‐matrix, wavelet analysis, regression analysis, filters, and several integrated approaches …”

Section: Introductionmentioning

confidence: 99%

“…[6][7][8][9][10] The third category performed the recovery by an interpolation technique with some known real spectra. 11,12 Finally, the fourth category used several methods, such as R-matrix, wavelet analysis, regression analysis, filters, and several integrated approaches. [13][14][15][16] Most previous works had a number of advantages and a number of drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Recovering spectral reflectance based on natural neighbor interpolation with model‐based metameric spectra of extreme points

Chou

Hsieh

Chen

2019

In this article, we proposed a new method based on natural neighbor interpolation to recover the spectral reflectance of objects from an image captured by a traditional Red‐Green‐Blue (RGB) digital camera. The concept of model‐based metameric spectra of eight extreme points in the standard RGB (sRGB) color gamut was further introduced to ensure that almost all test samples in the entire gamut can be simply and properly recovered without needing the extrapolation or any other auxiliary techniques. The quasi‐Newton method was used to estimate iteratively the optimal parameters of these metameric spectra, satisfying the constraints of the gamut extreme points. Several experiments were performed. The effectiveness of the method with and without the metameric spectra was evaluated, including some performance comparisons with the principal component analysis (PCA) method of transformational type (classic PCA and weighted PCA) and that of interpolation type. The results showed that the proposed method greatly enlarged the accurately applicable domain of the interpolation strategy and offered spectra with feasibility and naturalness much superior to the PCA‐based methods. The proposed method was obviously better than the conventional interpolation ones, and had a similar performance with the weighted PCA method in terms of color difference.

“…The goal of generating a reflectance distribution over the visible portion of the spectrum from a specified three‐dimensional color specifier (eg, tristimulus values) has been the subject of many investigations . The solution is not unique; there is an entire “metameric suite” of spectral reflectance functions that share a common triplet of tristimulus values for a particular illuminant .…”

Section: Introductionmentioning

confidence: 99%

Numerical methods for smoothest reflectance reconstruction

Burns

2019

Three numerical methods are presented for finding the smoothest reflectance curve associated with a given triplet of tristimulus values. The methods differ in how “smooth” is defined, and also differ in the domain of colors over which they are applicable. The first method is very quick and applies to any tristimulus values, but sometimes can yield reflectance curves with portions that fall outside the range 0 to 1. The second method applies to colors within the spectral locus (real colors) and guarantees that the reflectances produced are positive. The third method applies to colors within the object color solid (object colors) and guarantees that the reflectances fall within the range 0 to 1. The methods are shown to create reflectances that closely resemble those of real colors (natural and synthetic). Focus is given to implementing the numerical methods in very short MATLAB/Octave functions and to understanding the numerical behavior of the methods near the limits of their respective domains of applicability in terms of matrix conditioning and discretization artifacts.