Abstract
This paper presents a new image compression method based on the Inverse Difference Pyramid (IDP) decomposition, and one specific application of this method aimed at layered image transfer via a standard communication networks. A basic feature of the IDP method is that image decomposition is in the frequency domain coupled with the fact that every successive layer consists of increasing number of spectral coefficients, i.e. the pyramid, constituted by these coefficients values, is inverse. The higher pyramid levels correspond with higher image quality after image restoration. This permits, depending on the specific requirements, the image to be transferred and restored layer by layer until the required image quality is obtained. A special header of the compressed image data contains information about the number of pyramid levels, the coefficients used, etc. which can be used, when necessary, to transfer and restore only a selected part of the image only. In this case, the initial information (the data from the lower pyramid levels of the compressed image) is first transferred via a communication network. At the receiving side the image is visualized scaled down. At this point, the customer can, on demand, request to see the whole picture or a part of it, enlarged and with more details. The basic advantage of the method described for pyramidal image decomposition is that the received initial information for the lowest pyramid levels is used to upgrade the whole image while no part of the compressed image data is sent twice.
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Kountchev, R., Milanova, M., Ford, C., Kountcheva, R. Multi-layer Image Transmission with Inverse Pyramidal Decomposition. In: K. Halgamuge, S., Wang, L. (eds) Computational Intelligence for Modelling and Prediction. Studies in Computational Intelligence, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10966518_13
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DOI: https://doi.org/10.1007/10966518_13
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26071-4
Online ISBN: 978-3-540-32402-7
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