Abstract
Images are in its standard canonical form for a matrix have significant amount of redundant data. Thus image compression methods always under wide attention for efficient multimedia data transmission and storage. This paper concerned with the design of an optimized hybrid Neuro-Wavelet based model for image compression. In this design first the images are decomposed to various sub-band via wavelet transform and then they are fed to different supervised Neural Networks which are optimized with Linear Programming. The simulation results show the clear improvement over the existing methods objectively by PSNR and subjectively by visual appearance.
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Gambhir, D., Rajpal, N., Singh, V. (2011). Design of Optimized Neuro-Wavelet Based Hybrid Model for Image Compression. In: Nagamalai, D., Renault, E., Dhanuskodi, M. (eds) Advances in Digital Image Processing and Information Technology. DPPR 2011. Communications in Computer and Information Science, vol 205. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24055-3_10
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DOI: https://doi.org/10.1007/978-3-642-24055-3_10
Publisher Name: Springer, Berlin, Heidelberg
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