A Novel Neural Network Approach for Image Compression & Decompression

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 network with feed forward back propagation technique can be used for image compression. In this paper, the Bipolar Coding Technique and Levenberg-Marquardt (LM) algorithms are proposed and implemented for image compression and obtained the better results as compared to Principal Component Analysis (PCA) technique. It is observed that the Bipolar Coding and LM algorithm suits the best for image compression and processing applications .Keywords— Artificial Neural Network, PCA, Bipolar Coding, Levenberg-Marquardt Introduction Image compression refers to the task of reducing the amount of data required to store or transmit an image. At the system input, the image is encoded into its compressed form by the image coder. The compressed image may then be subjected to further digital processing, such as error control coding, encryption or multiplexing with other data sources, before being used to modulate the analog signal that is actually transmitted through the channel or stored in a storage medium. At the system output, the image is processed step by the step to undo each of the operations that were performed on it at the system input. At the final step, the image is decoded into its original uncompressed form by the image decoder. If the reconstructed image is identical to the original image the compression is said to be lossless, otherwise, it is lossy. Image compression addresses the problem of reducing the amount of information required to represent a digital image. It is a process intended to yield a compact representation of an image, thereby reducing the image storage transmission requirements. Every image will have redundant data. Redundancy means the duplication of data in the image. Either it may be repeating pixel across the image or pattern, which is repeated more frequently in the image. The image compression occurs by taking benefit of redundant information of in the image. Reduction of redundancy provides helps to achieve a saving of storage space of an image. Image compression is achieved when one or more of these redundancies are reduced or eliminated. In image compression, three basic data redundancies can be identified and exploited. Compression is achieved by the removal of one or more of the three basic data redundancies [1]. • Inter Pixel Redundancy • Coding Redundancy • Psycho Visual Redundancy