A Color Management Method for Photographed Image Files

2012 ◽  
Vol 461 ◽  
pp. 615-619
Author(s):  
Han Kun Ye
Keyword(s):  
Bp Neural Network ◽  
Color Space ◽  
Color Management ◽  
Sampled Data ◽  
Color Target ◽  
Engineering Requirement ◽  
Management Method ◽  
Image Production ◽  
Color Conversion ◽  
Bp Model

Color management method for photographed image files is the key to guarantee the color consistency in the succedent image production. Based on machine learning a new color management method for photographed image is advanced in the paper. First, the method selects the color shade districts of IT/8 color target as experiment sample and is taken to stand for the color space to decrease the calculation of the model. Second the method pretreats sampled data to a unitary field to satisfy the scope requirement of the input and output data of the BP neural network. Third, the active item which can adjust BP model step dynamically is used to increase convergence speed of the BP model. Finally, the experimental results indicates that the method can improve color conversion accuracy and can satisfy the engineering requirement in color management for photographed image files.

A Color Error Correction Mode for Digital Camera

2010 ◽  
Vol 44-47 ◽  
pp. 3706-3710
Author(s):  
Han Kun Ye
Keyword(s):  
Color Space ◽  
Digital Camera ◽  
Color Management ◽  
Experimental Sample ◽  
Color Target ◽  
Polynomial Curve ◽  
Engineering Requirement ◽  
Color Error ◽  
Image Production ◽  
Color Conversion

Digital camera is the one of the main devices in the computer and multimedia technology and its color management model is the key to guarantee the color consistency in the succedent image production and transfers. The paper presents a color conversion model for digital camera based on polynomial curve generation. First, color rendering principle of digital camera is analyzed. Then digital camera data is pretreated to a unitary field to deduce final model. Third, standard color target is taken for experimental sample and substitutes color blocks in color shade district for complete color space to solve the difficulties of experimental color blocks selecting; Fourth, the model using polynomial curve generation algorithm to correct color error is deduced; Finally, the realization and experiment results show that, compared with some methods which have relatively high accuracy, the algorithm can improve color conversion accuracy and can satisfy the engineering requirement in digital camera color management

Study on Color Space Conversion Based on RBF Neural Network

2010 ◽  
Vol 174 ◽  
pp. 28-31 ◽  
Author(s):  
Cong Jun Cao ◽  
Qiang Jun Liu
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Color Space ◽  
Color Management ◽  
Color Target ◽  
Color Space Conversion ◽  
Fast Training ◽  
Testing Data ◽  
Modeling Data ◽  
Target Data

The conversions of color spaces are core techniques of modern ICC color management and the study of color space conversion algorithm between L*a*b* and CMYK is valuable both in theory and in application. In this paper, firstly ECI2002 standard color target data are uniformly selected, including modeling data and testing data; secondly the models of color space conversions from CMYK to L*a*b* and from L*a*b* to CMYK are built based on Radial Basis Function (RBF) neural network; finally the precision of the models are evaluated. This research indicates that the RBF neural network is suitable for the color space conversions between CMYK and L*a*b*. The models’ building processes are simpler and more convenient; the network has fast training speed and good results. With the improvement of the modeling method, this method for color space conversion will have a broader application.

A Color Management Model for Color Sensors of Liquid Crystal Display

2010 ◽  
Vol 428-429 ◽  
pp. 394-397
Author(s):  
Xin Wu Li
Keyword(s):  
Liquid Crystal ◽  
Ant Colony Algorithm ◽  
Color Image ◽  
Liquid Crystal Display ◽  
Color Space ◽  
Ant Colony ◽  
Management Model ◽  
Color Management ◽  
Experimental Sample ◽  
Color Target

Color management for liquid crystal display is one of the key techniques in the color image reproduction. A new color management model is presented based on overcoming flaws and limitations of current ways of liquid crystal display color management . First, the paper takes standard color target for experimental sample, and substitutes color blocks in color shade district for complete color space. Second, data collecting method is introduced and some data bases for deducing the model are created. Then, ant colony algorithm is corrected to speed up model’s convergence and a new model for liquid crystal display color management based on improved ant colony algorithm is deduced and analyzed. Finally the experimental results show that the model can improve color management accuracy of liquid crystal display and can be used in its color management practically.

Research on Color Space Conversion Model between XYZ and RGB

2010 ◽  
Vol 428-429 ◽  
pp. 466-469
Author(s):  
Xin Wu Li
Keyword(s):  
Color Space ◽  
Digital Camera ◽  
Experimental Sample ◽  
Three Dimension ◽  
Conversion Error ◽  
Color Target ◽  
Conversion Model ◽  
Color Space Conversion ◽  
Correct Color ◽  
Color Conversion

Color space conversion for color digital camera is a key and difficult technique in the color reproduction information optics. A new color space conversion model based on subsectional fitting to correct color conversion error camera image is presented. First, color error sources and color rendering mechanism are analyzed in theory; then the paper takes standard color target for experimental sample and substitutes color blocks in color shade district for complete color space to solve the difficulties of experimental color blocks selecting; third the model uses subsectional fitting algorithm to built three dimension color conversion curve to correct color conversion error; Finally the experimental results show that the model can color space conversion accuracy of color digital camera and can be used in color conversion for digital camera practically.

Prediction of Sand Thickness Using Seismic Attributes Based on BP-Neural Network

2012 ◽  
Vol 524-527 ◽  
pp. 180-183
Author(s):  
Feng Gao
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Seismic Attributes ◽  
Bp Network ◽  
Sand Body ◽  
Precise Prediction ◽  
Energy Maximum ◽  
Bp Model ◽  
Rms Amplitude

Total energy, maximum peak amplitude and RMS amplitude are sensitive to sand body, and they are non-linear relations with sand thickness. In this study, a three-layer BP neural network is employed to build the prediction model. Nine samples were analyzed by three-layer BP network. The relationships were produced by BP network between sand thickness and the three seismic attributes. The precise prediction results indicate that the three-layer BP network based modeling is a practically very useful tool in prediction sand thickness. The BP model provided better accuracy in prediction than other methods.

Using ACES Look Modification Transforms (LMTs) in VFX Environments – Part 2: Gamut Mapping

2021 ◽  
Vol 2021 (3) ◽  
pp. 108-1-108-14
Author(s):  
Eberhard Hasche ◽  
Oliver Karaschewski ◽  
Reiner Creutzburg
Keyword(s):  
Dynamic Range ◽  
List Item ◽  
Color Space ◽  
High Dynamic Range ◽  
Target Color ◽  
List Type ◽  
Color Spaces ◽  
Gamut Mapping ◽  
Scale Down ◽  
Image Production

In modern moving image production pipelines, it is unavoidable to move the footage through different color spaces. Unfortunately, these color spaces exhibit color gamuts of various sizes. The most common problem is converting the cameras’ widegamut color spaces to the smaller gamuts of the display devices (cinema projector, broadcast monitor, computer display). So it is necessary to scale down the scene-referred footage to the gamut of the display using tone mapping functions [34].In a cinema production pipeline, ACES is widely used as the predominant color system. The all-color compassing ACES AP0 primaries are defined inside the system in a general way. However, when implementing visual effects and performing a color grade, the more usable ACES AP1 primaries are in use. When recording highly saturated bright colors, color values are often outside the target color space. This results in negative color values, which are hard to address inside a color pipeline. "Users of ACES are experiencing problems with clipping of colors and the resulting artifacts (loss of texture, intensification of color fringes). This clipping occurs at two stages in the pipeline: <list list-type="simple"> <list-item>- Conversion from camera raw RGB or from the manufacturer’s encoding space into ACES AP0</list-item> <list-item>- Conversion from ACES AP0 into the working color space ACES AP1" [1]</list-item> </list>The ACES community established a Gamut Mapping Virtual Working Group (VWG) to address these problems. The group’s scope is to propose a suitable gamut mapping/compression algorithm. This algorithm should perform well with wide-gamut, high dynamic range, scene-referred content. Furthermore, it should also be robust and invertible. This paper tests the behavior of the published GamutCompressor when applied to in- and out-ofgamut imagery and provides suggestions for application implementation. The tests are executed in The Foundry’s Nuke [2].

scholarly journals Optimization of Online Teaching Quality Evaluation Model Based on Hierarchical PSO-BP Neural Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Luxin Jiang ◽  
Xiaohui Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Online Teaching ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Particle Swarm ◽  
Teaching Quality ◽  
Particle Swarm Algorithm ◽  
Bp Model ◽  
Quality Evaluation Model

In the evaluation of teaching quality, aiming at the shortcomings of slow convergence of BP neural network and easy to fall into local optimum, an online teaching quality evaluation model based on analytic hierarchy process (AHP) and particle swarm optimization BP neural network (PSO-BP) is proposed. Firstly, an online teaching quality evaluation system was established by using the analytic hierarchy process to determine the weight of each subsystem and each index in the online teaching quality evaluation system and then combined with actual experience, the risk value of each index was constructed according to safety regulations. The regression model is established through BP neural network, and the weight and threshold of the model are optimized by the particle swarm algorithm. Based on the online teaching quality evaluation model of BP neural network, the parameters of the model are constantly adjusted, the appropriate function is selected, and the particle swarm algorithm which is used in the training and learning process of the neural network is optimized. The scientificity of the questionnaire was verified by reliability and validity test. According to the scoring results and combined with the weight coefficient of each indicator in the online course quality evaluation index system, the key factors affecting the quality of online courses were obtained. Based on the survey data, descriptive statistics, analysis of variance, and Pearson’s correlation coefficient method are used to verify the research hypothesis and obtain valuable empirical results. By comparing the model with the standard BP model, the results show that the accuracy of the PSO-BP model is higher than that of the standard BP model and PSO-BP effectively overcomes the shortcomings of the BP neural network.

scholarly journals Optimized Erosion Prediction with MAGA Algorithm Based on BP Neural Network for Submerged Low-Pressure Water Jet

2020 ◽  
Vol 10 (8) ◽  
pp. 2926
Author(s):  
Yanzhen Chen ◽  
Yihuai Hu ◽  
Shenglong Zhang ◽  
Xiaojun Mei ◽  
Qingguo Shi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Water Jet ◽  
Erosion Prediction ◽  
Pressure Water ◽  
Erosion Test ◽  
Bp Model

In order to accurately predict the erosion effect of underwater cleaning with an angle nozzle under different working conditions, this paper uses refractory bricks to simulate marine fouling as the erosion target, and studies the optimized erosion prediction model by erosion test based on the submerged low-pressure water jet. The erosion test is conducted by orthogonal experimental design, and experimental data are used for the prediction model. By combining with statistical range and variance analysis methods, the jet pressure, impact time and jet angle are determined as three inputs of the prediction model, and erosion depth is the output index of the prediction model. A virtual data generation method is used to increase the amount of input data for the prediction model. This paper also proposes a Mind-evolved Advanced Genetic Algorithm (MAGA), which has a reliable optimization effect in the verification of four stand test functions. Then, the improved back-propagating (BP) neural network prediction models are established by respectively using Genetic Algorithm (GA) and MAGA optimization algorithms to optimize the initial thresholds and weights of the BP neural network. Compared to the prediction results of the BP and GA-BP models, the R2 of the MAGA-BP model is the highest, reaching 0.9954; the total error is reduced by 47.31% and 35.01%; the root mean square error decreases by 51.05% and 31.80%; and the maximum absolute percentage error decreases by 65.79% and 64.01%, respectively. The average prediction accuracy of the MAGA-BP model is controlled within 3%, which has been significantly improved. The results show that the prediction accuracy of the MAGA-BP prediction model is higher and more reliable, and the MAGA algorithm has a good optimization effect. This optimized erosion prediction method is feasible.

Modified Non-Dominated Sorting Genetic Algorithm (MNSGA-II) Applied in Multi-Objective Optimization of a Coal-Fired Boiler Combustion

2013 ◽  
Vol 694-697 ◽  
pp. 2850-2855
Author(s):  
Ting Fang Yu ◽  
Xia Wang ◽  
Chun Hua Peng
Keyword(s):  
Genetic Algorithm ◽  
Heat Loss ◽  
Bp Neural Network ◽  
Nox Emissions ◽  
Multi Objective Optimization ◽  
Good Tool ◽  
Nsga Ii ◽  
Multi Objective ◽  
Comparison Operator ◽  
Bp Model

This paper discussed application of modified non-dominated sorting genetic algorithm-II (MNSGA-II) to multi-objective optimization of a coal-fired boiler combustion, the two objectives considered are minimization of overall heat loss and NOx emissions from coal-fired boiler. In the first step, BP neural network was proposed to establish a mathematical model predicting the NOx emissions & overall heat loss from the boiler. Then, BP model and the non-dominated sorting genetic algorithm II (NSGA-II) were combined to gain the optimal operating parameters. According to the problems such as premature convergence and uneven distribution of Pareto solutions exist in the application of NSGA-II, corresponding improvements in the crowded-comparison operator and crossover operator were performed. The optimal results show that MNSGA-II can be a good tool to solve the problem of multi-objective optimization of a coal-fired combustion, which can reduce NOx emissions and overall heat loss effectively for the coal-fired boiler. Compared with NSGA-II, the Pareto set obtained by the MNSGA-II shows a better distribution and better quality.

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