Chrominance space transformation method

A color space and color space technology, applied in the field of image processing, to improve learning ability and search speed, enhance generalization ability, improve average conversion accuracy and conversion speed

Active Publication Date: 2013-11-06
上海若古信息科技有限公司
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Problems solved by technology

However, when a particle is selected as the global optimal particle for multiple times in a row, the entire group of particles will quickly converge on the optimal particle, and may fall into a local optimal particle.

Method used

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Embodiment Construction

[0027] A chromaticity space transformation method according to the present invention will be described in detail below with reference to the accompanying drawings. The running platform of the present embodiment is Windows XP, uses Visual C++6.0 development environment, and graphics library uses OpenCV.

[0028] Step 1: Use a certain number of training samples to train the neural network. The neural network training process is as follows: figure 1 shown. Specifically: (1) Determine the structure of the neural network, and its topology is as follows: figure 2 Shown: BP neural network adopts 3-5-3 structure. The BP neural network used in this invention has 3 input nodes (corresponding to RGB color components respectively), 3 output nodes (corresponding to Lab components respectively) and a single hidden layer structure with 5 nodes, (2) select training samples: RGB The color space is evenly distributed in 10*10*10 small cubic spaces, and a total of 1000 samples are obtained. ...

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Abstract

The invention provides a chrominance space transformation method. According to the method, a re-assigned particle swarm optimization (RPSO) algorithm is used for correcting weights and threshold values of a back propagation (BP) neural network, the learning ability and the searching speed of the neural network are improved, and the generalization ability of the BP neural network is improved; a PSO algorithm is improved, the defect that the algorithm is trapped into local optimization easily is overcome, and the searching speed is accelerated; red-green-blue (RGB) color space values are converted into device-independent Lab color space values through the improved RPSO-BP neural network, and the average conversion accuracy and the conversion speed are improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a color space transformation method. Background technique [0002] RGB (Red Green Blue) is a space defined based on the colors recognized by the human eye, and can represent most colors. However, the RGB color space is generally not used in scientific research, because its details are difficult to digitally adjust. It puts the three quantities of hue, brightness, and saturation together, and it is difficult to separate them. [0003] The Lab color space is used for computer tone adjustment and color correction. It is independent of the color model of the device and has the characteristics of color uniformity. The Lab color space takes the coordinate Lab, where L represents brightness; the positive number of a represents red, and the negative end represents green; the positive number of b represents yellow, and the negative end represents blue. [0004] However, the trad...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/08
Inventor 高超杨乐胡凯刘春辉
Owner 上海若古信息科技有限公司
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