Digital Color Imaging

This paper surveys current technology and research in the area of digital color imaging. In order to establish the background and lay down terminology, fundamental concepts of color perception and measurement are first presented us-ing vector-space notation and terminology. Present-day color recording and reproduction systems are reviewed along with the common mathematical models used for representing these devices. Algorithms for processing color images for display and communication are surveyed, and a forecast of research trends is attempted. An extensive bibliography is provided

A Gaussian Process Model for Color Camera Characterization: Assessment in Outdoor Levantine Rock Art Scenes

[EN] In this paper, we propose a novel approach to undertake the colorimetric camera characterization procedure based on a Gaussian process (GP). GPs are powerful and flexible nonparametric models for multivariate nonlinear functions. To validate the GP model, we compare the results achieved with a second-order polynomial model, which is the most widely used regression model for characterization purposes. We applied the methodology on a set of raw images of rock art scenes collected with two different Single Lens Reflex (SLR) cameras. A leave-one-out cross-validation (LOOCV) procedure was used to assess the predictive performance of the models in terms of CIE XYZ residuals and Delta E-ab* color differences. Values of less than 3 CIELAB units were achieved for Delta E-ab*. The output sRGB characterized images show that both regression models are suitable for practical applications in cultural heritage documentation. However, the results show that colorimetric characterization based on the Gaussian process provides significantly better results, with lower values for residuals and Delta E-ab*. We also analyzed the induced noise into the output image after applying the camera characterization. As the noise depends on the specific camera, proper camera selection is essential for the photogrammetric work.This research is partly funded by the Research and Development Aid Program PAID-01-16 of the Universitat Politecnica de Valencia, through FPI-UPV-2016 Sub 1 grant.Molada-Tebar, A.; Riutort-Mayol, G.; Marqués-Mateu, Á.; Lerma, JL. (2019). A Gaussian Process Model for Color Camera Characterization: Assessment in Outdoor Levantine Rock Art Scenes. Sensors. 19(21):1-22. https://doi.org/10.3390/s19214610S1221921Ruiz, J. F., & Pereira, J. (2014). The colours of rock art. Analysis of colour recording and communication systems in rock art research. 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Uso combinado de la fotografía digital nocturna y de la fotogrametría en los procesos de documentación de petroglifos: el caso de Alcázar de San Juan (Ciudad Real, España). Virtual Archaeology Review, 8(17), 64. doi:10.4995/var.2017.6820Hong, G., Luo, M. R., & Rhodes, P. A. (2000). A study of digital camera colorimetric characterization based on polynomial modeling. Color Research & Application, 26(1), 76-84. doi:10.1002/1520-6378(200102)26:13.0.co;2-3Hung, P.-C. (1993). Colorimetric calibration in electronic imaging devices using a look-up-table model and interpolations. Journal of Electronic Imaging, 2(1), 53. doi:10.1117/12.132391Vrhel, M. J., & Trussell, H. J. (1992). Color correction using principal components. Color Research & Application, 17(5), 328-338. doi:10.1002/col.5080170507Bianco, S., Gasparini, F., Russo, A., & Schettini, R. (2007). A New Method for RGB to XYZ Transformation Based on Pattern Search Optimization. IEEE Transactions on Consumer Electronics, 53(3), 1020-1028. doi:10.1109/tce.2007.4341581Finlayson, G. D., Mackiewicz, M., & Hurlbert, A. (2015). Color Correction Using Root-Polynomial Regression. IEEE Transactions on Image Processing, 24(5), 1460-1470. doi:10.1109/tip.2015.2405336Connah, D., Westland, S., & Thomson, M. G. A. (2001). Recovering spectral information using digital camera systems. Coloration Technology, 117(6), 309-312. doi:10.1111/j.1478-4408.2001.tb00080.xLiang, J., & Wan, X. (2017). Optimized method for spectral reflectance reconstruction from camera responses. Optics Express, 25(23), 28273. doi:10.1364/oe.25.028273Heikkinen, V. (2018). Spectral Reflectance Estimation Using Gaussian Processes and Combination Kernels. IEEE Transactions on Image Processing, 27(7), 3358-3373. doi:10.1109/tip.2018.2820839Molada-Tebar, A., Lerma, J. L., & Marqués-Mateu, Á. (2017). Camera characterization for improving color archaeological documentation. Color Research & Application, 43(1), 47-57. doi:10.1002/col.22152Durmus, A., Moulines, É., & Pereyra, M. (2018). Efficient Bayesian Computation by Proximal Markov Chain Monte Carlo: When Langevin Meets Moreau. SIAM Journal on Imaging Sciences, 11(1), 473-506. doi:10.1137/16m1108340Ruppert, D., Wand, M. P., & Carroll, R. J. (2009). Semiparametric regression during 2003–2007. Electronic Journal of Statistics, 3(0), 1193-1256. doi:10.1214/09-ejs525Rock Art of the Mediterranean Basin on the Iberian Peninsulahttp://whc.unesco.org/en/list/874Direct Image Sensor Sigma SD15http://www.sigma-sd.com/SD15/technology-colorsensor.htmlRamanath, R., Snyder, W. E., Yoo, Y., & Drew, M. S. (2005). Color image processing pipeline. IEEE Signal Processing Magazine, 22(1), 34-43. doi:10.1109/msp.2005.1407713Stone, M. (1974). Cross-Validatory Choice and Assessment of Statistical Predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 111-133. doi:10.1111/j.2517-6161.1974.tb00994.xVazquez-Corral, J., Connah, D., & Bertalmío, M. (2014). Perceptual Color Characterization of Cameras. Sensors, 14(12), 23205-23229. doi:10.3390/s141223205Sharma, G., Wu, W., & Dalal, E. N. (2004). The CIEDE2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations. Color Research & Application, 30(1), 21-30. doi:10.1002/col.20070Lebrun, M., Buades, A., & Morel, J. M. (2013). A Nonlocal Bayesian Image Denoising Algorithm. SIAM Journal on Imaging Sciences, 6(3), 1665-1688. doi:10.1137/120874989Colom, M., Buades, A., & Morel, J.-M. (2014). Nonparametric noise estimation method for raw images. Journal of the Optical Society of America A, 31(4), 863. doi:10.1364/josaa.31.000863Sur, F., & Grédiac, M. (2015). Measuring the Noise of Digital Imaging Sensors by Stacking Raw Images Affected by Vibrations and Illumination Flickering. SIAM Journal on Imaging Sciences, 8(1), 611-643. doi:10.1137/140977035Zhang, Y., Wang, G., & Xu, J. (2018). Parameter Estimation of Signal-Dependent Random Noise in CMOS/CCD Image Sensor Based on Numerical Characteristic of Mixed Poisson Noise Samples. Sensors, 18(7), 2276. doi:10.3390/s18072276Naveed, K., Ehsan, S., McDonald-Maier, K. D., & Ur Rehman, N. (2019). A Multiscale Denoising Framework Using Detection Theory with Application to Images from CMOS/CCD Sensors. Sensors, 19(1), 206. doi:10.3390/s19010206Riutort-Mayol, G., Marqués-Mateu, Á., Seguí, A. E., & Lerma, J. L. (2012). Grey Level and Noise Evaluation of a Foveon X3 Image Sensor: A Statistical and Experimental Approach. Sensors, 12(8), 10339-10368. doi:10.3390/s120810339Marqués-Mateu, Á., Lerma, J. L., & Riutort-Mayol, G. (2013). Statistical grey level and noise evaluation of Foveon X3 and CFA image sensors. Optics & Laser Technology, 48, 1-15. doi:10.1016/j.optlastec.2012.09.034Chou, Y.-F., Luo, M. R., Li, C., Cheung, V., & Lee, S.-L. (2013). Methods for designing characterisation targets for digital cameras. Coloration Technology, 129(3), 203-213. doi:10.1111/cote.12022Shen, H.-L., Cai, P.-Q., Shao, S.-J., & Xin, J. H. (2007). Reflectance reconstruction for multispectral imaging by adaptive Wiener estimation. Optics Express, 15(23), 15545. doi:10.1364/oe.15.015545Molada-Tebar, A., Marqués-Mateu, Á., & Lerma, J. (2019). Camera Characterisation Based on Skin-Tone Colours for Rock Art Recording. Proceedings, 19(1), 12. doi:10.3390/proceedings201901901

Dominant Color Extraction with K-Means for Camera Characterization in Cultural Heritage Documentation

The camera characterization procedure has been recognized as a convenient methodology to correct color recordings in cultural heritage documentation and preservation tasks. Instead of using a whole color checker as a training sample set, in this paper, we introduce a novel framework named the Patch Adaptive Selection with K-Means (P-ASK) to extract a subset of dominant colors from a digital image and automatically identify their corresponding chips in the color chart used as characterizing colorimetric reference. We tested the methodology on a set of rock art painting images captured with a number of digital cameras. The characterization approach based on the P-ASK framework allows the reduction of the training sample size and a better color adjustment to the chromatic range of the input scene. In addition, the computing time required for model training is less than in the regular approach with all color chips, and obtained average color differences ΔE∗ab lower than two CIELAB units. Furthermore, the graphic and numeric results obtained for the characterized images are encouraging and confirms that the P-ASK framework based on the K-means algorithm is suitable for automatic patch selection for camera characterization purposes

Colorimetric and spectral analysis of rock art by means of the characterization of digital sensors

Tesis por compendio[ES] Las labores de documentación de arte rupestre son arduas y delicadas, donde el color desempeña un papel fundamental, proporcionando información vital a nivel descriptivo, técnico y cuantitativo . Tradicionalmente los métodos de documentación en arqueología quedaban restringidos a procedimientos estrictamente subjetivos. Sin embargo, esta metodología conlleva limitaciones prácticas y técnicas, afectando a los resultados obtenidos en la determinación del color. El empleo combinado de técnicas geomáticas, como la fotogrametría o el láser escáner, junto con técnicas de procesamiento de imágenes digitales, ha supuesto un notable avance. El problema es que, aunque las imágenes digitales permiten capturar el color de forma rápida, sencilla, y no invasiva, los datos RGB registrados por la cámara no tienen un sentido colorimétrico riguroso. Se requiere la aplicación de un proceso riguroso de tranformación que permita obtener datos fidedignos del color a través de imágenes digitales. En esta tesis se propone una solución científica novedosa y de vanguardia, en la que se persigue integrar el análisis espectrofotométrico y colorimétrico como complemento a técnicas fotogramétricas que permitan una mejora en la identificación del color y representación de pigmentos con máxima fiabilidad en levantamientos, modelos y reconstrucciones tridimensionales (3D). La metodología propuesta se basa en la caracterización colorimétrica de sensores digitales, que es de novel aplicación en pinturas rupestres. La caracterización pretende obtener las ecuaciones de transformación entre los datos de color registrados por la cámara, dependientes del dispositivo, y espacios de color independientes, de base física, como los establecidos por la Commission Internationale de l'Éclairage (CIE). Para el tratamiento de datos colorimétricos y espectrales se requiere disponer de un software de características técnicas muy específicas. Aunque existe software comercial, lo cierto es que realizan por separado el tratamiento digital de imágenes y las operaciones colorimétricas. No existe software que integre ambas, ni que además permita llevar a cabo la caracterización. Como aspecto fundamental, presentamos en esta tesis el software propio desarrollado, denominado pyColourimetry, siguiendo las recomendaciones publicadas por la CIE, de código abierto, y adaptado al flujo metodológico propuesto, de modo que facilite la independencia y el progreso científico sin ataduras comerciales, permitiendo el tratamiento de datos colorimétricos y espectrales, y confiriendo al usuario pleno control del proceso y la gestión de los datos obtenidos. Adicinalmente, en este estudio se expone el análisis de los principales factores que afectan a la caracterización tales como el sensor empleado, los parámetros de la cámara durante la toma, la iluminación, el modelo de regresión, y el conjunto de datos empleados como entrenamiento del modelo. Se ha aplicado un modelo de regresión basado en procesos Gaussianos, y se ha comparado con los resultados obtenidos mediante polinomios. También presentamos un nuevo esquema de trabajo que permite la selección automática de muestras de color, adaptado al rango cromático de la escena, que se ha denominado P-ASK, basado en el algoritmo de clasificación K-means. Los resultados obtenidos en esta tesis demuestran que el proceso metodológico de caracterización propuesto es altamente aplicable en tareas de documentación y preservación del patrimonio cultural en general, y en arte rupestre en particular. Se trata de una metodología de bajo coste, no invasiva, que permite obtener el registro colorimétrico de escenas completas. Una vez caracterizada, una cámara digital convencional puede emplearse para la determinación del color de forma rigurosa, simulando un colorímetro, lo que permitirá trabajar en un espacio de color de base física, independiente del dispositivo y comparable con[CA] Les tasques de documentació gràfica d'art rupestre són àrdues i delicades, on el color compleix un paper fonamental, proporcionant informació vital a nivell descriptiu, t\`ecnic i quantitatiu.Tradicionalment els mètodes de documentació en arqueologia quedaven restringits a procediments estrictament subjectius, comportant limitacions pràctiques i tècniques, afectant els resultats obtinguts en la determinació de la color. L'ús combinat de tècniques geomàtiques, com la fotogrametria o el làser escàner, juntament amb tècniques de processament i realç d'imatges digitals, ha suposat un notable avanç. Tot i que les imatges digitals permeten capturar el color de forma ràpida, senzilla, i no invasiva, les dades RGB proporcionades per la càmera no tenen un sentit colorimètric rigorós. Es requereix l'aplicació d'un procés rigorós de transformació que permeti obtenir dades fidedignes de la color a través d'imatges digitals. En aquesta tesi es proposa una solució científica innovadora i d'avantguarda, en la qual es persegueix integrar l'anàlisi espectrofotomètric i colorimètric com a complement a tècniques fotogramètriques que permetin una millora en la identificació de la color i representació de pigments amb màxima fiabilitat en aixecaments, models i reconstruccions tridimensionals 3D. La metodologia proposada es basa en la caracterització colorimètrica de sensors digitals, que és de novell aplicació en pintures rupestres. La caracterització pretén obtenir les equacions de transformació entre les dades de color registrats per la càmera, dependents d'el dispositiu, i espais de color independents, de base física, com els establerts per la Commission Internationale de l'Éclairage (CIE). Per al tractament de dades colorimètriques i espectrals de forma rigorosa es requereix disposar d'un programari de característiques tècniques molt específiques. Encara que hi ha programari comercial, fan per separat el tractament digital d'imatges i les operacions colorimètriques. No hi ha programari que integri totes dues, ni que permeti dur a terme la caracterització. Com a aspecte addicional i fonamental, vam presentar el programari propi que s'ha desenvolupat, denominat pyColourimetry, segons les recomanacions publicades per la CIE, de codi obert, i adaptat al flux metodológic proposat, de manera que faciliti la independència i el progrés científic sense lligams comercials, permetent el tractament de dades colorimètriques i espectrals, i conferint a l'usuari ple control del procés i la gestió de les dades obtingudes. A més, s'exposa l'anàlisi dels principals factors que afecten la caracterització tals com el sensor emprat, els paràmetres de la càmera durant la presa, il¿luminació, el model de regressió, i el conjunt de dades emprades com a entrenament d'el model. S'ha aplicat un model de regressió basat en processos Gaussians, i s'han comparat els resultats obtinguts mitjançant polinomis. També vam presentar un nou esquema de treball que permet la selecció automàtica de mostres de color, adaptat a la franja cromàtica de l'escena, que s'ha anomenat P-ASK, basat en l'algoritme de classificació K-means. Els resultats obtinguts en aquesta tesi demostren que el procés metodològic de caracterització proposat és altament aplicable en tasques de documentació i preservació de el patrimoni cultural en general, i en art rupestre en particular. Es tracta d'una metodologia de baix cost, no invasiva, que permet obtenir el registre colorimètric d'escenes completes. Un cop caracteritzada, una càmera digital convencional pot emprar-se per a la determinació de la color de forma rigorosa, simulant un colorímetre, el que permetrà treballar en un espai de color de base física, independent d'el dispositiu i comparable amb dades obtingudes mitjançant altres càmeres que tambè estiguin caracteritzades.[EN] Cultural heritage documentation and preservation is an arduous and delicate task in which color plays a fundamental role. The correct determination of color provides vital information on a descriptive, technical and quantitative level. Classical color documentation methods in archaeology were usually restricted to strictly subjective procedures. However, this methodology has practical and technical limitations, affecting the results obtained in the determination of color. Nowadays, it is frequent to support classical methods with geomatics techniques, such as photogrammetry or laser scanning, together with digital image processing. Although digital images allow color to be captured quickly, easily, and in a non-invasive way, the RGB data provided by the camera does not itself have a rigorous colorimetric sense. Therefore, a rigorous transformation process to obtain reliable color data from digital images is required. This thesis proposes a novel technical solution, in which the integration of spectrophotometric and colorimetric analysis is intended as a complement to photogrammetric techniques that allow an improvement in color identification and representation of pigments with maximum reliability in 3D surveys, models and reconstructions. The proposed methodology is based on the colorimetric characterization of digital sensors, which is of novel application in cave paintings. The characterization aims to obtain the transformation equations between the device-dependent color data recorded by the camera and the independent, physically-based color spaces, such as those established by the Commission Internationale de l'Éclairage (CIE). The rigorous processing of color and spectral data requires software packages with specific colorimetric functionalities. Although there are different commercial software options, they do not integrate the digital image processing and colorimetric computations together. And more importantly, they do not allow the camera characterization to be carried out. Therefore, as a key aspect in this thesis is our in-house pyColourimetry software that was developed and tested taking into account the recommendations published by the CIE. pyColourimetry is an open-source code, independent without commercial ties; it allows the treatment of colorimetric and spectral data and the digital image processing, and gives full control of the characterization process and the management of the obtained data to the user. On the other hand, this study presents a further analysis of the main factors affecting the characterization, such as the camera built-in sensor, the camera parameters, the illuminant, the regression model, and the data set used for model training. For computing the transformation equations, the literature recommends the use of polynomial equations as a regression model. Thus, polynomial models are considered as a starting point in this thesis. Additionally, a regression model based on Gaussian processes has been applied, and the results obtained by means of polynomials have been compared. Also, a new working scheme was reported which allows the automatic selection of color samples, adapted to the chromatic range of the scene. This scheme is called P-ASK, based on the K-means classification algorithm. The results achieved in this thesis show that the proposed framework for camera characterization is highly applicable in documentation and conservation tasks in general cultural heritage applications, and particularly in rock art painting. It is a low-cost and non-invasive methodology that allows for the colorimetric recording from complete image scenes. Once characterized, a conventional digital camera can be used for rigorous color determination, simulating a colorimeter. Thus, it is possible to work in a physical color space, independent of the device used, and comparable with data obtained from other cameras that are also characterized.Thanks to the Universitat Politècnica de València for the FPI scholarshipMolada Tebar, A. (2020). Colorimetric and spectral analysis of rock art by means of the characterization of digital sensors [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/160386TESISCompendi

Color and Texture Analysis of Textiles Using Image Acquisition and Spectral Analysis in Calibrated Sphere Imaging System-I

Funding This research received no external funding. Acknowledgments We are also grateful to Manas Sarkar, ITC, HKPU for providing cotton samples with varied textures and Dystar, Hong Kong, for generously providing us with dye samples. We are thankful to for the experimental support from new fiber science and IoT Lab, OUTR sponsored by TEQIP-3 seed money and MODROB (/9-34/RIFDMO DPOLICY-1/2018-19).Peer reviewedPublisher PD

Print engine color management using customer image content

The production of quality color prints requires that color accuracy and reproducibility be maintained to within very tight tolerances when transferred to different media. Variations in the printing process commonly produce color shifts that result in poor color reproduction. The primary function of a color management system is maintaining color quality and consistency. Currently these systems are tuned in the factory by printing a large set of test color patches, measuring them, and making necessary adjustments. This time-consuming procedure should be repeated as needed once the printer leaves the factory. In this work, a color management system that compensates for print color shifts in real-time using feedback from an in-line full-width sensor is proposed. Instead of printing test patches, this novel attempt at color management utilizes the output pixels already rendered in production pages, for a continuous printer characterization. The printed pages are scanned in-line and the results are utilized to update the process by which colorimetric image content is translated into engine specific color separations (e.g. CIELAB-\u3eCMYK). The proposed system provides a means to perform automatic printer characterization, by simply printing a set of images that cover the gamut of the printer. Moreover, all of the color conversion features currently utilized in production systems (such as Gray Component Replacement, Gamut Mapping, and Color Smoothing) can be achieved with the proposed system

Expanding Dimensionality in Cinema Color: Impacting Observer Metamerism through Multiprimary Display

Television and cinema display are both trending towards greater ranges and saturation of reproduced colors made possible by near-monochromatic RGB illumination technologies. Through current broadcast and digital cinema standards work, system designs employing laser light sources, narrow-band LED, quantum dots and others are being actively endorsed in promotion of Wide Color Gamut (WCG). Despite artistic benefits brought to creative content producers, spectrally selective excitations of naturally different human color response functions exacerbate variability of observer experience. An exaggerated variation in color-sensing is explicitly counter to the exhaustive controls and calibrations employed in modern motion picture pipelines. Further, singular standard observer summaries of human color vision such as found in the CIE’s 1931 and 1964 color matching functions and used extensively in motion picture color management are deficient in recognizing expected human vision variability. Many researchers have confirmed the magnitude of observer metamerism in color matching in both uniform colors and imagery but few have shown explicit color management with an aim of minimized difference in observer perception variability. This research shows that not only can observer metamerism influences be quantitatively predicted and confirmed psychophysically but that intentionally engineered multiprimary displays employing more than three primaries can offer increased color gamut with drastically improved consistency of experience. To this end, a seven-channel prototype display has been constructed based on observer metamerism models and color difference indices derived from the latest color vision demographic research. This display has been further proven in forced-choice paired comparison tests to deliver superior color matching to reference stimuli versus both contemporary standard RGB cinema projection and recently ratified standard laser projection across a large population of color-normal observers

Appearance-based image splitting for HDR display systems

High dynamic range displays that incorporate two optically-coupled image planes have recently been developed. This dual image plane design requires that a given HDR input image be split into two complementary standard dynamic range components that drive the coupled systems, therefore there existing image splitting issue. In this research, two types of HDR display systems (hardcopy and softcopy HDR display) are constructed to facilitate the study of HDR image splitting algorithm for building HDR displays. A new HDR image splitting algorithm which incorporates iCAM06 image appearance model is proposed, seeking to create displayed HDR images that can provide better image quality. The new algorithm has potential to improve image details perception, colorfulness and better gamut utilization. Finally, the performance of the new iCAM06-based HDR image splitting algorithm is evaluated and compared with widely spread luminance square root algorithm through psychophysical studies

Design and Construction of a Multispectral Camera for Spectral and Colorimetric Reproduction

Multi-spectral imaging and spectral reflectance reconstruction can be used in cultural-heritage institutes to digitalize their collections for documentation purposes. It can be used to simulate artwork under any lighting condition, and to analyze colorants that were used. The basic idea of a multi-spectral imaging system is to sub-sample spectral reflectance factor, producing results similar to a spectrophotometer. The sampled data are used to reconstruct reflectance for the visible spectrum. In this thesis, a wide band multispectral camera was designed and constructed to achieve high spectral and color accuracy as well as high image quality. Noise propagation theory was introduced and tested. A seven channel band- pass filter set was modeled using Gaussian functions and optimized to yield high spectral and colorimetric reproduction accuracy as well as low colori- metric noise. Single and sandwich filters were selected from o!-the-shelf absorption filters using the Gaussian bandpass filter model. Experiments were conducted to test the spectral, color and noise performance of the novel sandwich filters and compared with interference filters. The novel sandwich fil- ters led to increased colorimetric accuracy along with a reduction colorimetric noise. This imaging system will be used as part of a recommended workflow for museum archiving, and will be an important addition to the spectral imaging capabilities at MCSL