International Journal of Computer Science Issues

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Image compression helps in storing the transmitted data in proficient way by decreasing its redundancy. This technique helps in transferring more digital or multimedia data over internet as it increases the storage space. It is important to maintain the image quality even if it is compressed to certain extent. Depends upon this the image compression is classified into two categories: lossy and lossless image compression. There are many lossy digital image compression techniques exists. Among this Wavelet Transform based image compression is the most familiar one. The good picture quality can be retrieved if Wavelet-based image compression technique is used for compression and also provides better compression ratio. In the past few years Artificial Neural Network becomes popular in the field of image compression. This paper proposes a technique for image compression using modified Self-Organizing Map (SOM) based vector quantization. Self-Organizing Feature Map (SOFM) algorithm is a type of neural network model which consists of one input and one output layer. Each input node is connected with output node by adaptive weights. By modifying the weights between input nodes and output nodes, SOFM generate codebook for vector quantization. If the compression is performed using Vector Quantization (VQ), then it results in enhanced performance in compression than any other existing algorithms. Vector Quantization is based on the encoding of scalar quantities. The experimental result shows that the proposed technique obtained better PSNR value end also reduces Mean Square Error.