The explosive growths in data exchanges have necessitated the development of new methods of image compression including use of learning based techniques. The learning based systems aids proper compression and retrieval of the image segments. Learning systems like. Artificial Neural Networks (ANN) have established their efficiency and reliability in achieving image compression. In this work, two approaches to use ANNs in Feed Forward (FF) form and another based on Self Organizing Feature Map (SOFM) is proposed for digital image compression. The image to be compressed is first decomposed into smaller blocks and passed to FFANN and SOFM networks for generation of codebooks. The compressed images are reconstructed using a composite block formed by a FFANN and a Discrete Cosine Transform (DCT) based compression-decompression system. Mean Square Error (MSE), Compression ratio (CR) and Peak Signal-to-Noise Ratio (PSNR) are used to evaluate the performance of the system.
Recent Technology for Digital Image Compression Alogorithm