Colorimetric characterization can reduce the distortion of image color information in the process of reproduction of the display device, so as to ensure that the same image can be accurately transmitted and reproduced. In order to realize the precise color characteristics of LCD, this paper uses the steepest descent method to optimize the parameters of GOG model, and establishes the color space conversion model from RGB to CIEXYZ and analyzes the model accuracy. The experimental results show that the maximum color difference of the model is 4.7494, and the average color difference is 2.7435, which can meet the accuracy needs of LCD colorimetric characterization.
In order to solve the problem that the same image has different display results on different monitors, the color characteristic of the display is needed. In this paper, the least square method is used to fit the experimental data，the polynomial regression method is used to build the RGB to CIEXYZ color conversion model of the display, and the accuracy of the model is analyzed. The experimental results show that the accuracy of the cubic polynomials curve model is the highest, the maximum color difference is 5.2862, and the average color difference is 2.6510.
Black point is the point at which RGB's single channel digital drive value is 0. Due to the problem of light leakage of liquid-crystal displays (LCDs), black point’s luminance value is not 0, this phenomenon bring some errors to colorimetric characterization of LCDs, especially low luminance value driving greater sampling effect. This paper describes the characteristic accuracy of polynomial model method and the effect of black point on accuracy, the color difference accuracy is given. When considering the black point in the characteristics equation, the maximum color difference is 3.246, the maximum color difference than without considering the black points reduced by 2.36. The experimental results show that the accuracy of LCDs colorimetric characterization can be improved, if the effect of black point is eliminated properly.
Image quality evaluation is a classic research topic, the goal is to design the algorithm, given the subjective feelings consistent with the evaluation value. This paper mainly introduces several typical reference methods of Mean Squared Error(MSE), Peak Signal to Noise Rate(PSNR), Structural Similarity Image Metric(SSIM) and feature similarity(FSIM) of objective evaluation methods. The different evaluation methods are tested by Matlab, and the advantages and disadvantages of these methods are obtained by analyzing and comparing them.MSE and PSNR are simple, but they are not considered to introduce HVS characteristics into image quality evaluation. The evaluation result is not ideal. SSIM has a good correlation and simple calculation ,because it is considered to the human visual effect into image quality evaluation,However the SSIM method is based on a hypothesis,The evaluation result is limited. The FSIM method can be used for test of gray image and color image test, and the result is better. Experimental results show that the new image quality evaluation algorithm based on FSIM is more accurate.
The colorimetric characterization of the display can achieve the purpose of precisely controlling the color of the monitor. This paper describes an improved method for estimating the gamma value of liquid-crystal displays (LCDs) without using a measurement device was described by Xiao et al. It relies on observer’s luminance matching by presenting eight half-tone patterns with luminance from 1/9 to 8/9 of the maximum value of each color channel. Since the previous method lacked partial low frequency information, we partially replaced the half-tone patterns. A large number of experiments show that the color difference is reduced from 3.726 to 2.835, and our half-tone pattern can better estimate the visual gamma value of LCDs.
In order to improve the accuracy of colorimetric characterization of liquid crystal display, tone matrix model in color management modeling of display characterization is established by using constrained least squares for quadratic polynomial fitting, and find the relationship between the RGB color space to CIEXYZ color space; 51 sets of training samples were collected to solve the parameters, and the accuracy of color space mapping model was verified by 100 groups of random verification samples. The experimental results showed that, with the constrained least square method, the accuracy of color mapping was high, the maximum color difference of this model is 3.8895, the average color difference is 1.6689, which prove that the method has better optimization effect on the colorimetric characterization of liquid crystal display.
Proc. SPIE. 10322, Seventh International Conference on Electronics and Information Engineering
KEYWORDS: Visualization, Software development, Associative arrays, Chemical elements, Document management, Data communications, Binary data, Process modeling, Standards development, Information architecture
Research on the design and the overall structure of Darwin Information Typing Architecture to reflect the advantages of Darwin Information Typing Architecture in the digital publishing application. Topic-oriented fundamental principles and the mapping structure in Darwin Information Typing Architecture meet the needs of depth usage of digital publication content, achieved the principle "once produced, multiple release. DITA can be used in digital publishing throughout the process to achieve flexible reuse of delivery publications. By DITA rendering, multiple formats delivery publications could be achieved. Darwin Typing Information Architecture already has a lot of typical applications both domestic and foreign, with the rapid development of digital publishing industry, Darwin Typing Information Architecture will play a bigger role in the field of digital publishing.
A new single image super-resolution method based on self-similarity across different scales and pyramid model is proposed. In order to enrich the diversity of the training patches but not increase the computational complexity, we rotate the low resolution input image by a certain angle from 0° to 90° and down-sample them into 2 layers pyramid model respectively. However, most self-similarity super-resolution algorithms was carried out by the fixed size of patch. So, in this paper we observe the effect of patch size using the various patch size then pick out the most appropriate patch size. During the mapping process, we use the Fast Library for Approximate Nearest Neighbors (FLANN) to search the corresponding nine closest patches in high-frequency pyramid then carry out Gaussian weighted (SSD), which can avoid the occasionality and mismatch by using the nearest neighbor strategy. Finally, the local constraint and the iterative back projection algorithm are adopted to optimize the reconstructed image. Experimental results validate that the algorithm is better than the traditional algorithm in visual effects and computational complexity.
In liquid crystal display (LCD) colorimetric characterization, it needs to convert RGB the device-dependent color space to CIEXYZ or CIELab the device-independent color space. Namely establishing the relationship between RGB and CIE using the data of device color and the corresponding data of CIE. Thus a color automatic message acquisition software is designed. We use openGL to fulfill the full screen display function, write c++ program and call the Eyeone equipment library functions to accomplish the equipment calibration, set the sample types, and realize functions such as sampling and preservation. The software can drive monitors or projectors display the set of sample colors automatically and collect the corresponding CIE values. The sample color of RGB values and the acquisition of CIE values can be stored in a text document, which is convenient for future extraction and analysis. Taking the cubic polynomial as an example, each channel is sampled of 17 sets using this system. And 100 sets of test data are also sampled. Using the least square method we can get the model. The average of color differences are around 2.4874, which is much lower than the CIE2000 commonly required level of 6.00.The successful implementation of the system saves the time of sample color data acquisition, and improves the efficiency of LCD colorimetric characterization.
In this paper, a new method based on machine vision is proposed for the defects of the traditional manual inspection of the quality of printed matter. With the aid of on line array CCD camera for image acquisition, using stepper motor as a sampling of drive circuit. Through improvement of driving circuit, to achieve the different size or precision image acquisition. In the terms of image processing, the standard image registration algorithm then, because of the characteristics of CCD-image acquisition, rigid body transformation is usually used in the registration, so as to achieve the detection of printed image.
Now many image super-resolution methods suppose that the optical flows between images should be
computed accurately. But really it is very difficult to get them and the models of imaging systems are
unknown almost. Thurs perturbation errors always occur in the image super-resolution model. The
paper proposes an improved image super-resolution algorithm based on total least squares method. The
average image based on images is used as regularized penalty for posteriori probability model. The
paper presents the improved Rayleigh quotient format for energy objective function. Then a conjugate
gradient algorithm is used to minimize the modified Rayleigh quotient function. The method can
minimize two the errors from the sampled low-resolution images and in that perturbation system matrix
of high-resolution reconstruction. The test results showed that the algorithm is stable for the
perturbation system matrix.
The regularization parameter plays a crucial role in the quality of a restored super-resolution image. In this paper, we
propose a method for parametric regularization based on the incomplete orthogonalization method. The method truncates
the Arnoldi recurrence. Specifically, an integer <i>k</i> is selected so that it is necessary to keep only the <i>k</i> previous orthogonal
vectors for Arnoldi process. The others are not needed in the process and may be discarded. The method can be
inexpensively computed by the incomplete orthogonal process. Thus a convenient way for choosing the regularization
parameter is presented.
Super-resolution image restoration is often known to be an ill-posed inverse and large scale problem. The
regularization parameter plays a crucial role in the quality of the restored image. Although generalized cross-validation is
a popular tool for computing a regularized parameter, it has been rarely applied to super-resolution image restoration
problems until recently. A major difficulty lies in the implementation of generalized cross-validation which requires the
costly computation and the evaluation of the trace of an inverse matrix. In this paper numerical approximate techniques
are used to reduce the computational complexity. We employ Gauss quadrature to compute approximately the
cross-validation function. The evaluation of the trace of the inverse matrix is replaced by stochastic trace so as to
alleviate the problem. Further, Lancros algorithm and Galerkin equation is used to evaluate the stochastic trace. Our
results show that the method is an effective and robust.
In this paper, we consider the estimation of the unknown parameter for the problem of reconstructing a high resolution
image from multiple under-sampled, shifted, degraded frames. L-curve criterion is used to estimate regularization
parameters. However, the computational of the L-curve is quiet costly for large-size problems. The paper proposes an
efficient approximate technique based on Gauss quadrature rule. The technique translates some matrix computation into
Gauss quadrature with singular decomposition. It can reduce the computational complexity of the L-curve.
The L-curve and its curvature are often applied to determine a suitable value of the regularization parameter when solving ill-conditioned linear systems of equations in super-resolution image reconstruction. However, the computation of the L-curve and its curvature is quite costly. In this paper both L-curve and its curvature can be computed fairly inexpensively by partial Arnoldi process applied to the matrix of the given linear system of equations in super-resolution image reconstruction. Through the Arnoldi process the techniques can generate orthogonal bases for the Krylov subspaces, which is a small and condensed Hessenberg matrix. The paper presents the simple solution in super-resolution image reconstruction by the Hessenberg matrix and presents the method for quickly computing L-curve and its curvature.
The point spread function (PSF) parameters of the imaging system are not often known a prior in super-resolution
enhancement applications. In our super-resolution algorithm, we identify the PSF and regularization parameters from the
raw data using the generalized cross-validation method (GCV). Motivated by the success of GCV in identifying optimal
smoothing parameters for image restoration, we have extended the method to the problem of estimating blur parameters.
To reduce the computational complexity of GCV, we propose efficient approximation techniques based on the Arnoldi
process. The Arnoldi process can yield a small and condensed Hessenberg matrix which is orthogonal bases of the
Krylov subspaces. Experiments are presented which demonstrate the effectiveness and robustness of our method.