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Some assessments on applications of fuzzy clustering techniques in multimedia compression systems

Published: 10 January 2020 Publication History

Abstract

Data compression is the process of reducing the amount of necessary memory for the representation of a given piece of information. This process is of great utility especially in digital storage and transmission of the multimedia information and it typically involves various encoding/decoding schemes. In this work we will be primarily focused on some compression schemes which employ specific forms of clustering known as fuzzy clustering. In the data mining context, fuzzy clustering is a versatile tool which analyzes heterogeneous collections of data providing insights on the underlying structures involving the concept of partial membership. Several models employing the fuzzy clustering techniques in data compression systems are demonstrated and image compression based on fuzzy transforms for compression and decompression of color videos is described in details.

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MEDES '19: Proceedings of the 11th International Conference on Management of Digital EcoSystems
November 2019
350 pages
ISBN:9781450362382
DOI:10.1145/3297662
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 10 January 2020

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Author Tags

  1. Fuzzy clustering algorithms
  2. compression rate
  3. data compression
  4. image clustering
  5. image compression

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MEDES '19

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MEDES '19 Paper Acceptance Rate 41 of 102 submissions, 40%;
Overall Acceptance Rate 267 of 682 submissions, 39%

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