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An Improved Neural Image Compression Approach with Cluster Based Preprocessing


Affiliations
1 Fakir Mohan University, Balasore-756019, Odisha, India
 

The convergence time for training back propagation neural network for image compression is slow as compared to other traditional image compression techniques. This article proposes a pre-processing technique i.e. Pre-processed Back propagation neural image compression (PBN) with an enhancement in performance measures like better convergence time with respect to decoded picture quality and compression ratios as compared to simple back-propagation based image compression and other image coding techniques for color images.

Keywords

Back Propagation, Bipolar Sigmoid, Vector Quantization.
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  • An Improved Neural Image Compression Approach with Cluster Based Preprocessing

Abstract Views: 359  |  PDF Views: 165

Authors

Ashanta Ranjan Routray
Fakir Mohan University, Balasore-756019, Odisha, India
Munesh Chandra Adhikary
Fakir Mohan University, Balasore-756019, Odisha, India

Abstract


The convergence time for training back propagation neural network for image compression is slow as compared to other traditional image compression techniques. This article proposes a pre-processing technique i.e. Pre-processed Back propagation neural image compression (PBN) with an enhancement in performance measures like better convergence time with respect to decoded picture quality and compression ratios as compared to simple back-propagation based image compression and other image coding techniques for color images.

Keywords


Back Propagation, Bipolar Sigmoid, Vector Quantization.