Abstract
The popularity of multimedia on the Internet has created a heavy load on bandwidth. Consequently, content compression and bandwidth reduction have become significant topics recently. An appropriate codebook design is an essential and valuable principle for Vector Quantization (VQ). This investigation presents a new image compression method called INTSOM, which relies on Hierarchical Self-Organizing Map (HSOM) and adopts LBG for speeding up. For a two-layer neural network, INTSOM first employs LBG to determine the number of first layer neurons, and uses an estimation function to determine the number of second layer neurons dynamically. A modified SOM is then performed to compress each sub-map. Experimental results indicate that INTSOM has better overall capability (time-cost and quality) than LBG, SOM and HSOM.
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Tsai, CF., Ju, JH. (2009). INTSOM: Gray Image Compression Using an Intelligent Self-Organizing Map. In: Nakamatsu, K., Phillips-Wren, G., Jain, L.C., Howlett, R.J. (eds) New Advances in Intelligent Decision Technologies. Studies in Computational Intelligence, vol 199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00909-9_4
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DOI: https://doi.org/10.1007/978-3-642-00909-9_4
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