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Data Security in the Cloud via Artificial Intelligence with Vector Quantization for Image Compression

  • Srinivasa Kiran Gottapu
  • Pranav Vallabhaneni
Conference paper
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)

Abstract

In this chapter two cloud compression techniques are used on an image, namely, Vector Quantization (VQ) and Feed Forward Neural Network (FFNN). VQ is used along K-Mean clustering to initiate the centroids and form the code-book. The FFNN in this algorithm has an architecture specification of 64 nodes in the input and the output layer along with 16 hidden layers with 16 nodes each. The VQ is applied first on the input image to achieve some compression and then the VQ compressed image is fed as an input to the FFNN network for additional compression. A set of observations for compression are recorded for different values of K (number of centroids) with a tile size 8. The results are obtained for different values of K such as 50, 100, 150, 200, 250, 500 and 1000. The proposed algorithm gives a compression ratio of about 2 and an acceptable PSNR of about 20 dB for the standard testing image Lena.

Keywords

Image compression Vector Quantization (VQ) K-Mean Clustering Centroids Codebook Feed Forward Neural Network (FFNN) 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Srinivasa Kiran Gottapu
    • 1
  • Pranav Vallabhaneni
    • 2
  1. 1.Department of Electrical EngineeringUniversity of North TexasDentonUSA
  2. 2.Department of Computer Science and EngineeringSir C. R. Reddy College of EngineeringEluruIndia

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