[Research on Visual Perception-Referenced Compression Method for Multi-Spectral Data with High-Fidelity].

Aim In order to maintain the chromaticity precision in the process of linear compression of the multispectral data, a visual perception-referenced compression method (VPCM) based on the chroma gradient (refer to the partial derivative of chroma to wavelength) is proposed. Method The method firstly successfully developed the transfer functions which could synchronously fusion the spectral features and chromaticity characteristics of human visuals based on the nonlinear analytic feature of human visual system. For further improvement the transfer function, a modified optimizing function was developed to help find out the optimal transfer direction for different sample sets. If the transfer function was finally settled, it will be applied to transforming the spectral data of the sample set (Γ(S)=C). Then the transformed spectral data of the sample set will be compressed with high chromatic accuracy by the principle components analysis method. After that, the compressed data will be reconstructed through inverse transformation (Γ(-1)(C)=), while the reconstructed spectral data will be using to evaluate the effective of the proposed VPCM method. Result Four groups typical and representative sample sets were chosen to test the effective of the proposed method. The CIELab color difference in the D50/2° calculates condition and a proposed mean metamerism index (MMI) calculated with 75 groups typical light sources (including tungsten, fluorescent and LED lamp) was adopted as evaluating metrics. Eventually, the comparative experiment involving several existing methods Lab-PQR and 2-XYZ indicates that the proposed VPCM hold the best chromatic accuracy both for metric MMI and the average color difference ΔE(ab) when compared with Lab-PQR and 2-XYZ, and the spectral accuracy was calculated between Lab-PQR and 2-XYZ with Lab-PQR maintained the highest spectral accuracy. Conclusion The proposed VPCM can preserve high compression chromatic precision at the price of small loss of spectral precision and possess good colorimetric stability under variable reference conditions. It is very applicable for some application fields which require compressing of the multi-spectral data with high chromatic accuracy.

[Iterated Tikhonov Regularization for Spectral Recovery from Tristimulus].

Reflective spectra in a multispectral image can objectively and originally represent color information due to their high dimensionality, illuminant independent and device independent. Aiming to the problem of loss of spectral information when the spectral data reconstructed from three-dimensional colorimetric data in the trichromatic camera-based spectral image acquisition system and its subsequent problem of loss of color information, this work proposes an iterated Tikhonov regularization to reconstruct the reflectance spectra. First of all, according to relationship between the colorimetric value and the reflective spectra in the colorimetric theory, this work constructs a spectral reconstruction equation which can reconstruct high dimensional spectral data from three dimensional colorimetric data acquired by the trichromatic camera. Then, the iterated Tikhonov regularization, inspired by the idea of the pseudo inverse Moore-Penrose, is used to cope with the linear ill-posed inverse problem during solving the equation of reconstructing reflectance spectra. Meanwhile, the work also uses the L-curve method to obtain an optimal regularized parameter of the iterated Tikhonov regularization by training a set of samples. Through these methods, the ill condition of the spectral reconstruction equation can be effectively controlled and improved, and subsequently loss of spectral information of the reconstructed spectral data can be reduced. The verification experiment is performed under another set of training samples. The experimental results show that the proposed method reconstructs the reflective spectra with less spectral information loss in the trichromatic camera-based spectral image acquisition system, which reflects in obvious decreases of spectral errors and colorimetric errors compared with the previous method.

[Research on the Training Samples Selection for Spectral Reflectance Reconstruction Based on Principal Component Analysis].

The composition of training samples set is an important influence factor of spectral reflectance reconstruction process. Representative color samples selection for learning-based spectral reflectance reconstruction is discussed in this paper. A method based on Principal Component Analysis (PCA) is proposed to perform sample selection. First of all, a part of samples are selected according to the minimum Euclidean distance criteria in terms of camera response value from a large number of samples, which aim to ensure the similarity between training samples and target samples. Then the PCA data processing method is applied to these samples after removing the duplicate samples. The samples with larger principal component loadings are regarded as the representative color samples. Different thresholds for each principal component are used to make decision whether the loading of sample is large enough. In order to validate the proposed method, the selected samples are used as training samples to recover the spectral reflectance of color patches. A real multi-channel imaging system by loading broadband color filters in front of lens is used in the experiment to acquire the multi-channel image dataset. In this paper the pseudo-inverse method is employed to reconstruct spectral reflectance of target color patches. It is shown that the proposed method is superior to the previous methods in spectral reconstruction accuracy and can meet the requirements of high precision color reproduction.

[Research on Developping the Colorimetric Correction Method Based on Wide Gamut Spectral Color Dataset].

In cross-media digital color imaging workflow, the colorimetric information transform among various digital medias always suffers from large transform errors, due to the difference among the lighting illuminants and the digital devices. Colorimetric correction aims at avoiding the colorimetric transform mismatch and thus improving the color transform accuracy comprehensively. Till now, two kinds of colorimetric transform methods have been proposed, which are the chromatic adaptation transform and the regression-based transform. However, since the color gamut of the training samples of such two method are both small, adopting those methods in colorimetric domain always leads to large colorimetric transform errors for the high saturated color regions. In this research, in order to reduce the large correction error in high saturated color regions, a modified colorimetric correction method basing on a wide gamut spectral dataset was proposed. The wide gamut spectral dataset was built by comprehensively collecting and producing typical spectral color samples and could provide optimal training samples for the existing regression based colorimetric correction model, with the help of gamut partition and optimal color purity choosing. By modifying the existing method with such samples, the colorimetric correction performance obviously improved. The experimental result shows that the modified colorimetric correction method performs significantly better than the existing methods and the colorimetric correction errors are successfully reduced by around 15% according to proposed method in form of CIEDE2000 color difference, while as for the high saturated color regions the reduction rate of the colorimetric correction errors approximately grows to 40%. The authors believe that the proposed method will provided effective support for the development of digital color imaging in near future.

[Optimization Study of Non-Contact Color Measurement Parameters].

Spectral data or chrominance data acquired with color measurement device is able to objectively characterize the true color information of the object. As to the measuring target that without direct contact with the measuring instrument, it needs to adopt the non-contact color measurement method to obtain the color information.The spectral irradiance theoretical analysis shows that the changes of the color illumination distance, measuring distance and exposure time have greater impact on the measurement results. However, there is no in-depth study on the influence of the changes of measuring parameters for the measured results. In order to get the most accurate measurement results in the process of non-contact color measurement, parameter optimization study based on orthogonal experiment was proposed. Experiment with different combinations of parameters was conducted to obtain the color information of ColorChecker via PR705 spectroradiometer with range analysis and variance analysis method.Experimental results show that the minimum color difference of measured value and the standard value is 0.878 8ΔE, and the maximum color difference is 1.543 1ΔE. Data results show that the proposed method can effectively select the best combination of parameters, and to analyze the impact of various parameters on the result.

Research on the Development of Light Blending Model for Smart LED Lighting.

The color of the LED smart light is tunable by its inner equipped micro-processing systems. Therefore, it could provide significant improvement for the smart lighting conditions, such as museum lighting and home lighting. At present, the limitation of the current lighting blending technology remarkably affects the application of smart lighting technology and people could not make full use of the adjustability of the smart luminaries. In this research, a novel light blending model was proposed based on BP neural network and active set algorithm. The models could effectively simulate the nonlinear relationship between the device control values of the smart light and the output radiance spectrum of the light. Particularly, a BP neural network-based forward model for LED light blending was firstly proposed, which could accurately calculate the spectral radiance power distribution from the device control values. Afterwards, based on forward model, an active set algorithm-based backward model was developed, which could precisely predict the device control values from the desired spectral radiance power distribution. The experimental result indicates that the proposed method could accurately achieve the light blending controlling of smart LED light, with a CIEUCS Duv value of 0.002 7, which is significantly smaller than the just noticeable difference value of human vision. The authors believe that the proposed method will provided effective support for the development of smart LED lighting in near future.

Research on Assessment Methods of Spectral Reflectance Data Quality for Computer Color Matching.

Spectral reflectance data quality is important for computer color matching. There are two existing methods for evaluating the quality­spectral reflectance method and K/S method, which are too complex to apply. In this paper, 45°/0° and d/8° geometric conditions are used in the measurement of spectral reflectance of the offset ink samples printed on coated paper and silver-foiled paper while improvement on the geometric condition is made on the basis of the spectral reflectance method. Moreover, a new evaluation method­lightness and chromaticity comparative method is put forward, and comparison is made among the three methods. The results show that both 45°/0° and d/8° are feasible in the measurement of spectral reflectance of coated paper; however the former one cannot meet the requirement of spectral reflectance measurement of silver-foiled paper. In addition, as to d/8° Specular Component Included (SCI), when the silver-foiled paper is taken as the substrate, the reflectance of transparent white ink samples are smaller than that of other primary inks; and abnormal intersections appear in the curves of cyan and magenta ink respectively at the concentration of 60%, resulting in a poor spectra quality at high ink concentration; In the figure of lightness and chromaticity curves, there is significant divergence of the cyan and magenta ink curves from the referenced coated paper. In conclusion, the spectral reflectance of the transparent ink should be greater than or at least equal to other primary inks, and the maximum concentration of cyan and magenta should be limited; when the coated paper with good diffusion performance is taken as the reference, the comparative analysis is more intuitive than the two existing methods.

[The study on spectral reflectance reconstruction based on wideband multi-spectral acquisition system].

The multispectral image acquisition oriented to reproduction requests that the data is device independent and scenes independent, and can realize the characterization of the original color information. Aiming at disturbance, noise error of system, and the requirement for training samples' typical representative and correlation, the authors proposed orthogonal regression spectral algorithm and training samples selection algorithm based on subspace tracking, through the mapping function between the spectral space and color space, by selecting the best samples in typical representative and correlation samples between target samples and selected samples. The modified Sinar 75H trichromatic digital camera combined with bandpass filter glasses were used for experiment, the data show that our method has higher spectral and chromaticity accuracy, the training samples selected by subspace tracking method are uniformly distributed in the sample space, and have good orthogonality. The statistics experimental results indicate that the performance of the proposed method is obviously better than that of previous method, in both color difference error and spectral reflectance error.

[Research on developping the spectral dataset for Dunhuang typical colors based on color constancy].

The present paper aims at developping a method to reasonably set up the typical spectral color dataset for different kinds of Chinese cultural heritage in color rendering process. The world famous wall paintings dating from more than 1700 years ago in Dunhuang Mogao Grottoes was taken as typical case in this research. In order to maintain the color constancy during the color rendering workflow of Dunhuang culture relics, a chromatic adaptation based method for developping the spectral dataset of typical colors for those wall paintings was proposed from the view point of human vision perception ability. Under the help and guidance of researchers in the art-research institution and protection-research institution of Dunhuang Academy and according to the existing research achievement of Dunhuang Research in the past years, 48 typical known Dunhuang pigments were chosen and 240 representative color samples were made with reflective spectral ranging from 360 to 750 nm was acquired by a spectrometer. In order to find the typical colors of the above mentioned color samples, the original dataset was devided into several subgroups by clustering analysis. The grouping number, together with the most typical samples for each subgroup which made up the firstly built typical color dataset, was determined by wilcoxon signed rank test according to the color inconstancy index comprehensively calculated under 6 typical illuminating conditions. Considering the completeness of gamut of Dunhuang wall paintings, 8 complementary colors was determined and finally the typical spectral color dataset was built up which contains 100 representative spectral colors. The analytical calculating results show that the median color inconstancy index of the built dataset in 99% confidence level by wilcoxon signed rank test was 3.28 and the 100 colors are distributing in the whole gamut uniformly, which ensures that this dataset can provide reasonable reference for choosing the color with highest color constancy during the color rendering process of Dunhuang cultural heritage.