New Approaches for Image Compression Using Neural Network

Vilas H. Gaidhane, Vijander Singh, Yogesh V. Hote, Mahendra Kumar

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DOI: 10.4236/jilsa.2011.34025                

Abstract

An image consists of large data and requires more space in the memory. The large data results in more transmission time from transmitter to receiver. The time consumption can be reduced by using data compression techniques. In this technique, it is possible to eliminate the redundant data contained in an image. The compressed image requires less memory space and less time to transmit in the form of information from transmitter to receiver. Artificial neural net- work with feed forward back propagation technique can be used for image compression. In this paper, the Bipolar Coding Technique is proposed and implemented for image compression and obtained the better results as compared to Principal Component Analysis (PCA) technique. However, the LM algorithm is also proposed and implemented which can acts as a powerful technique for image compression. It is observed that the Bipolar Coding and LM algorithm suits the best for image compression and processing applications.

Keywords

Image Compression, Feed Forward Back Propagation Neural Network, Principal Component Analysis (PCA), Levenberg-Marquardt (LM) Algorithm, PSNR, MSE

Conflicts of Interest

The authors declare no conflicts of interest.

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