The JPEG still picture compression standard
Communications of the ACM - Special issue on digital multimedia systems
IEEE Transactions on Computers
Optimal pyramidal and subband decompositions for hierarchical coding of noisy and quantized images
IEEE Transactions on Image Processing
Use of nonlinear principal component analysis and vector quantization for image coding
IEEE Transactions on Image Processing
Image compression by self-organized Kohonen map
IEEE Transactions on Neural Networks
Medical image compression based on vector quantization with variable block sizes in wavelet domain
Computational Intelligence and Neuroscience - Special issue on Computational Intelligence in Biomedical Science and Engineering
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This paper presents a novel lossy compression scheme for medical images by using an incremental self–organized map (ISOM). Three neural networks for lossy compression scheme are comparatively examined: Kohonen map, multi-layer perceptron (MLP) and ISOM. In the compression process of the proposed method, the image is first decomposed into blocks of 8(8 pixels. Two-dimensional discrete cosine transform (2D-DCT) coefficients are computed for each block. The dimension of DCT coefficients vectors (codewords) is reduced by low-pass filtering. Huffman coding is applied to the indexes of codewords obtained by the ISOM. In the decompression process, inverse operations of each stage of the compression are performed in the opposite way. It is observed that the proposed method gives much better compression rates.