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.
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.
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.
KEYWORDS: Information architecture, Associative arrays, Standards development, Chemical elements, Visualization, Document management, Software development, Data communications, Binary data, Process modeling
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.