High‐end colorimetric display characterization using an adaptive training set

Colantoni, Philippe; Thomas, Jean‐Baptiste; Hardeberg, Jon Yngve

doi:10.1889/jsid19.8.520

Cited by 19 publications

(18 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…27,42 Therefore it may be beneficial to investigate if these effects can be accounted for by using the Masking Model or the Modified Masking Model. 43,44 Further improvements may also be achieved by including a black offset in the parametrization models, and corrections for flare. 2,31,43…”

Section: R Ecom M Enda Ti Ons F or F U Ture Wo Rkmentioning

confidence: 99%

See 1 more Smart Citation

Improving color reproduction accuracy of an OLED‐based mobile display

Kirchner

Lans

Perales

et al. 2017

Color Research & Application

View full text Add to dashboard Cite

For improving color reproduction accuracy of mobile displays, we recently developed a generic model for device-specific display characterization model that also accounts for the influence of illuminance from ambient light. In the present article, this MDCIM model (Mobile Display Characterization and Illumination Model) is applied to a Samsung Galaxy S4 display, representing OLED displays. The performance of the model was tested by determining the values of all model parameters using publicly available technical data only. We organized visual tests under various ambient illuminance levels from 600 to 3000 lux. Seven observers compared the color of displayed images with the color of physical samples. With the MDCIM method, the quality of the color match was shown to improve considerably as compared to using only device-independent encoding color space. On a five-point scale to quantify color reproduction accuracy, the MDCIM resulted in more than 1 unit improvement at 1000 lux illuminance. At lower and higher illuminance, the improvement was even larger. Color reproduction accuracy was found to be at least reasonable, according to the subjective assessment of visual observers, for more than 75% of the samples when using the MDCIM method, but only 20% or less when using the common device-independent encoding color space. K E Y W O R D Scolor accuracy, color measurement, display technology, sRGB color space

show abstract

Section: R Ecom M Enda Ti Ons F or F U Ture Wo Rkmentioning

confidence: 99%

“…43,44 Further improvements may also be achieved by including a black offset in the parametrization models, and corrections for flare. 2,31,43…”

Section: R Ecom M Enda Ti Ons F or F U Ture Wo Rkmentioning

confidence: 99%

Improving color reproduction accuracy of an OLED‐based mobile display

Kirchner

Lans

Perales

et al. 2017

Color Research & Application

View full text Add to dashboard Cite

show abstract

“…Various types of relationships between pixel values of the image and the reference color values have been already established in the literature. For instance, color calibrations using least-squares polynomial regression [21][22][23], linear regression [24], and neural networks [25] were previously established, and they have been applied successfully in different areas such as dentistry [26], photography [27,28], printing technologies [29], and the food industry [24,30]. However, there is insufficient information about color calibration for agricultural applications.…”

Section: Introductionmentioning

confidence: 99%

Color Calibration of Proximal Sensing RGB Images of Oilseed Rape Canopy via Deep Learning Combined with K-Means Algorithm

et al. 2019

View full text Add to dashboard Cite

Plant color is a key feature for estimating parameters of the plant grown under different conditions using remote sensing images. In this case, the variation in plant color should be only due to the influence of the growing conditions and not due to external confounding factors like a light source. Hence, the impact of the light source in plant color should be alleviated using color calibration algorithms. This study aims to develop an efficient, robust, and cutting-edge approach for automatic color calibration of three-band (red green blue: RGB) images. Specifically, we combined the k-means model and deep learning for accurate color calibration matrix (CCM) estimation. A dataset of 3150 RGB images for oilseed rape was collected by a proximal sensing technique under varying illumination conditions and used to train, validate, and test our proposed framework. Firstly, we manually derived CCMs by mapping RGB color values of each patch of a color chart obtained in an image to standard RGB (sRGB) color values of that chart. Secondly, we grouped the images into clusters according to the CCM assigned to each image using the unsupervised k-means algorithm. Thirdly, the images with the new cluster labels were used to train and validate the deep learning convolutional neural network (CNN) algorithm for an automatic CCM estimation. Finally, the estimated CCM was applied to the input image to obtain an image with a calibrated color. The performance of our model for estimating CCM was evaluated using the Euclidean distance between the standard and the estimated color values of the test dataset. The experimental results showed that our deep learning framework can efficiently extract useful low-level features for discriminating images with inconsistent colors and achieved overall training and validation accuracies of 98.00% and 98.53%, respectively. Further, the final CCM provided an average Euclidean distance of 16.23 ∆E and outperformed the previously reported methods. This proposed technique can be used in real-time plant phenotyping at multiscale levels.

show abstract

“…This relationship is generally calculated by acquiring with the considered device a reference color chart with well-known color values in standard illumination conditions [5]. Various types of relationships have been already implemented in the literature, such as multidimensional lookup tables with interpolation [6][7][8], least-squares polynomial regressions with various polynomial orders, advanced regression modeling [9], neural networks [10,11] and also human-observation-based models [12]. These color calibration methods have been implemented successfully for various imaging systems: computer vision systems for meat quality evaluation [9,13], dermatoscopic imaging systems [14][15][16], colposcopes [17], commercial digital cameras for various applications such as dentistry [18] or color advice for home décor [19], displays [7,12], or printers [8,11].…”

Section: Introductionmentioning

confidence: 99%

Color calibration of an RGB camera mounted in front of a microscope with strong color distortion

et al. 2013

View full text Add to dashboard Cite

This paper aims at showing that performing color calibration of an RGB camera can be achieved even in the case where the optical system before the camera introduces strong color distortion. In the present case, the optical system is a microscope containing a halogen lamp, with a nonuniform irradiance on the viewed surface. The calibration method proposed in this work is based on an existing method, but it is preceded by a three-step preprocessing of the RGB images aiming at extracting relevant color information from the strongly distorted images, taking especially into account the nonuniform irradiance map and the perturbing texture due to the surface topology of the standard color calibration charts when observed at micrometric scale. The proposed color calibration process consists first in computing the average color of the color-chart patches viewed under the microscope; then computing white balance, gamma correction, and saturation enhancement; and finally applying a third-order polynomial regression color calibration transform. Despite the nonusual conditions for color calibration, fairly good performance is achieved from a 48 patch Lambertian color chart, since an average CIE-94 color difference on the color-chart colors lower than 2.5 units is obtained.

show abstract

High‐end colorimetric display characterization using an adaptive training set

Cited by 19 publications

References 46 publications

Improving color reproduction accuracy of an OLED‐based mobile display

Improving color reproduction accuracy of an OLED‐based mobile display

Color Calibration of Proximal Sensing RGB Images of Oilseed Rape Canopy via Deep Learning Combined with K-Means Algorithm

Color calibration of an RGB camera mounted in front of a microscope with strong color distortion

Contact Info

Product

Resources

About