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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
E.M. Saad, A.A. Abdelwahab, M.A. Deyab, Using feed forward multilayer neural network and vector quantization as an image data compression technique, in Proceedings of the Third IEEE Symposium on Computers and Communications, 1998, ISCC’98, Athens, 1998, pp. 554–558. https://doi.org/10.1109/ISCC.1998.702592
V.D. Raut, S. Dholay, Analyzing image compression with efficient transforms & multistage vector quantization using radial basis function neural network, in 2015 IEEE International Conference on Engineering and Technology (ICETECH), Coimbatore, 2015, pp. 1–6. https://doi.org/10.1109/ICETECH.2015.7275009
P. Natu, S. Natu, T. Sarode, Hybrid image compression using VQ on error image, in 2017 International Conference on Intelligent Communication and Computational Techniques (ICCT), Jaipur, 2017, pp. 173–176. https://doi.org/10.1109/INTELCCT.2017.8324040
W.K. Yeo et al., Grayscale medical image compression using feedforward neural networks, in 2011 IEEE International Conference on Computer Applications and Industrial Electronics (ICCAIE), Penang, 2011, pp. 633–638. https://doi.org/10.1109/ICCAIE.2011.6162211
P.K. Shah, R.P. Pandey, R. Kumar, Vector quantization with codebook and index compression, in 2016 International Conference System Modeling & Advancement in Research Trends (SMART), Moradabad, 2016, pp. 49–52. https://doi.org/10.1109/SYSMART.2016.7894488
W. Zhang, H. Li, X. Long, An improved classified vector quantization for medical image, in 2015 IEEE Tenth Conference on Industrial Electronics and Applications (ICIEA), Auckland, 2015, pp. 238–241. https://doi.org/10.1109/ICIEA.2015.7334118
A.J. Hussain, A. Al-Fayadh, N. Radi, Image compression techniques: a survey in lossless and lossy algorithms. Neurocomputing 300, 44–69 (2018)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Gottapu, S.K., Vallabhaneni, P. (2020). Data Security in the Cloud via Artificial Intelligence with Vector Quantization for Image Compression. In: Haldorai, A., Ramu, A., Mohanram, S., Onn, C. (eds) EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-19562-5_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-19562-5_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19561-8
Online ISBN: 978-3-030-19562-5
eBook Packages: EngineeringEngineering (R0)