Abstract—Neural Networks are based on the parallel architecture and inspired from human brains. Neural networks are a form of multiprocessor computer system, with simple processing elements, a high degree of interconnection, simple scalar messages and adaptive interaction between elements. One such application is image compression. Image compression is a process which minimizes the size of an image file without degrading the quality of the image to an unacceptable level. It also reduces the time required for images to be sent over the internet or downloaded from web pages. This paper proposes an Improved Backpropagation Neural Network Technique, for lossless image compression. The system also proves that the improved Backpropagation Neural Network Technique works better than the existing Huffman Coding Technique for lossless image compression by considering X-Ray images based on three metrics such as compression ratio, transmission time and compression performance. Experimental results are presented and compared.

Index Terms—Backpropagation, Huffman Coding, Image Compression, Neural Network, X-Ray.

N. Senthilkumaran is with the School of Computer Science and Engineering, Bharathiar University, Coimbatore - 641 046, India.(e-mail:senthilkumaran@ieee.org).
J. Suguna is with the Computer Science Department, Vellalar College for Women, Erode, India.

Cite: N. Senthilkumaran and J. Suguna, "Neural Network Technique for Lossless Image Compression Using X-Ray Images," International Journal of Computer and Electrical Engineering vol. 3, no. 1, pp. 17-23, 2011.