An Approach of Optimizing Singular Value of YCbCr Color Space with q-Gaussian Function in Image Processing

  • Abhisek PaulEmail author
  • Paritosh Bhattacharya
  • Santi Prasad Maity
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 27)


To increase performance of Radial Basis Functions (RBFs) in Artificial Neural Network q-Gaussian Radial Basis Function (q-GRBF) is introduced to optimize singular value of Y, Cb and Cr color image components. Various radial basis functions such as Gaussian Radial Basis Function (GRBF), Multi Quadratic Radial Basis Function (MCRBF), Inverse Multi Quadratic Radial Basis Function (IMCRBF) and Cosine Radial Basis Function (CRBF) are also introduced and compared with singular values of Y, Cb and Cr component of color images. Simulation and analysis shows that q-Gaussian Radial Basis Function gives lesser error and better result compared to the other radial basis functions in artificial neural network.


Gaussian RBF Multi Quadratic RBF Inverse Multi Quadratic RBF Cosine RBF q-Gaussian RBF Radial Basis Function Artificial Neural Network 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Abhisek Paul
    • 1
    Email author
  • Paritosh Bhattacharya
    • 1
  • Santi Prasad Maity
    • 2
  1. 1.Department of Computer Science and EngineeringNational Institute of TechnologyAgartalaIndia
  2. 2.Department of Information TechnologyBengal Engineering and Science UniversityShibpurIndia

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