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Morphological Transform for Image Compression

Abstract

A new method for image compression based on morphological associative memories (MAMs) is presented. We used the MAM to implement a new image transform and applied it at the transformation stage of image coding, thereby replacing such traditional methods as the discrete cosine transform or the discrete wavelet transform. Autoassociative and heteroassociative MAMs can be considered as a subclass of morphological neural networks. The morphological transform (MT) presented in this paper generates heteroassociative MAMs derived from image subblocks. The MT is applied to individual blocks of the image using some transformation matrix as an input pattern. Depending on this matrix, the image takes a morphological representation, which is used to perform the data compression at the next stages. With respect to traditional methods, the main advantage offered by the MT is the processing speed, whereas the compression rate and the signal-to-noise ratio are competitive to conventional transforms.

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Correspondence to Oleksiy Pogrebnyak.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Guzmán, E., Pogrebnyak, O., Yañez, C. et al. Morphological Transform for Image Compression. EURASIP J. Adv. Signal Process. 2008, 426580 (2008). https://doi.org/10.1155/2008/426580

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Keywords

  • Traditional Method
  • Quantum Information
  • Processing Speed
  • Transformation Matrix
  • Discrete Wavelet