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Fuzzy Clustering and Hyperanalytic Wavelet Transform for Lossy Image Compression: A Review

By T. M. Jayanthi and D. Sundararajan

Abstract

Abstract- Clustering techniques are mostly unsupervised methods that can be used to organize data into groups based on similarities among the individual data items. Most clustering algorithms do not rely on assumptions common to conventional statistical methods, such as the underlying statistical distribution of data, and therefore they are useful in situations where little prior knowledge exists. The potential of clustering algorithms to reveal the underlying structures in data can be exploited in a wide variety of applications, including classification, image processing, pattern recognition, modeling and identification[3][4][6]. In this paper study of fuzzy clustering methods, and the properties of Hyper analytic wavelet transform (HWT) that are used for image compression is explained. An image compression technique based on HWT in combination with SPIHT encoding is explained and results are compared to the results obtained by using normal DWT with same SPIHT encoding[1]. Keywords- Clustering, , Discrete wavelet transform(DWT), Hyperanalytic wavelet transform(HWT), Set partitioning in Hierarchical tree (SPITH) I

Year: 2014
OAI identifier: oai:CiteSeerX.psu:10.1.1.413.3020
Provided by: CiteSeerX
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