Preprocessing operators for image compression using cellular neural networks

O. Moreira-Tamayo, J. Pineda de Gyvez

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    3 Citations (Scopus)
    135 Downloads (Pure)


    Cellular neural networks (CNN) have traditionally been used to perform nonlinear operations on images such as edge detection, hole filling, etc. However, algorithms for image compression using CNN have scarcely been explored. This paper presents new templates and novel algorithms to perform basic operations used for image compression. Thy include wavelet subband decomposition, computation of parameters for bit allocation, quantization and bit extraction. These algorithms are hardware oriented and exploit the massive parallelism provided by the CNN. Compression is an important and widely used operation in image processing. Therefore, the algorithms presented here expand the realm of CNN applications. This feature is especially important for the widespread use of CNN as a multiple purpose image processor
    Original languageEnglish
    Title of host publicationProceedings of the IEEE International Conference on Neural Networks, 1996, 3-6 June 1996, Washington, DC
    Place of PublicationNew York
    PublisherInstitute of Electrical and Electronics Engineers
    ISBN (Print)0-7803-3210-5
    Publication statusPublished - 1996


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