Abstract
A new approach for image compression based on the GHSOM model has been proposed in this paper. The SOM has some problems related to its fixed topology and its lack of representation of hierarchical relations among input data. The GHSOM solves these limitations by generating a hierarchical architecture that is automatically determined according to the input data and reflects the inherent hierarchical relations among them. These advantages can be utilized to perform a compression of an image, where the size of the codebook (leaf neurons in the hierarchy) is automatically established. Moreover, this hierarchy provides a different compression at each layer, where the deeper the layer, the lower the rate compression and the higher the quality of the compressed image. Thus, different trade-offs between compression rate and quality are given by the architecture. Also, the size of the codebooks and the depth of the hierarchy can be controlled by two parameters. Experimental results confirm the performance of this approach.
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Palomo, E.J., Domínguez, E., Luque, R.M., Muñoz, J. (2011). Lossy Image Compression Using a GHSOM. In: Cabestany, J., Rojas, I., Joya, G. (eds) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21498-1_1
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DOI: https://doi.org/10.1007/978-3-642-21498-1_1
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