Quantized Principal Component Analysis with Applications to Low-Bandwidth Image Compression and Communication
نویسندگان
چکیده
منابع مشابه
Quantized Principal Component Analysis with Applications to Low-bandwidth Image Compression and Communication
Abstract. In this paper we show how Principal Component Analysis can be mapped to a quantized domain in an optimal manner. In particular, given a low-bandwidth communication channel over which a given set of data is to be transmitted, we show how to best compress the data. Applications to image compression are described and examples are provided that support the practical soundness of the propo...
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Principal component analysis (PCA) is an effective tool for spectral decorrelation of hyperspectral imagery, and PCA-based spectral transforms have been employed successfully in conjunction with JPEG2000 for hyperspectral-image compression. However, the computational cost of determining the data-dependent PCA transform is high due to its traditional eigendecomposition implementation which requi...
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ژورنال
عنوان ژورنال: Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications
سال: 2005
ISSN: 2188-4730,2188-4749
DOI: 10.5687/sss.2005.283