A predictive algorithm for multimedia data compression
A predictive algorithm for multimedia data compression
In lossless image compression, many prediction methods are proposed so far to achieve better compression performance/complexity trade off. In this paper, we concentrate on some well-known and widely used low-complexity algorithms exploited in many modern compression systems, including MED, GAP, Graham, Ljpeg, DARC, and GBSW. This paper proposes a new gradient-based tracking and adapting technique that outperforms some existing methods. This paper aims to design an efficient highly adaptive predictor that can be incorporated in modeling step of image compression systems. This claim is proved by testing the proposed method upon a wide variety of images with different characteristics. Six special sets of images including face, sport, texture, sea, text, and medical constitute our dataset.
Image compression, Lossless compression, Multimedia, Predictive coding
103-115
Rad, Reza Moradi
7cf68458-1991-4d35-96dc-b6433caeb6f9
Attar, Abdolrahman
f5efd538-042a-4647-9d46-1370d3049b72
Shahbahrami, Asadollah
a254cee0-48b1-4a87-b6ab-49bf8aee412d
March 2013
Rad, Reza Moradi
7cf68458-1991-4d35-96dc-b6433caeb6f9
Attar, Abdolrahman
f5efd538-042a-4647-9d46-1370d3049b72
Shahbahrami, Asadollah
a254cee0-48b1-4a87-b6ab-49bf8aee412d
Rad, Reza Moradi, Attar, Abdolrahman and Shahbahrami, Asadollah
(2013)
A predictive algorithm for multimedia data compression.
Multimedia Systems, 19 (2), .
(doi:10.1007/s00530-012-0282-0).
Abstract
In lossless image compression, many prediction methods are proposed so far to achieve better compression performance/complexity trade off. In this paper, we concentrate on some well-known and widely used low-complexity algorithms exploited in many modern compression systems, including MED, GAP, Graham, Ljpeg, DARC, and GBSW. This paper proposes a new gradient-based tracking and adapting technique that outperforms some existing methods. This paper aims to design an efficient highly adaptive predictor that can be incorporated in modeling step of image compression systems. This claim is proved by testing the proposed method upon a wide variety of images with different characteristics. Six special sets of images including face, sport, texture, sea, text, and medical constitute our dataset.
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Published date: March 2013
Keywords:
Image compression, Lossless compression, Multimedia, Predictive coding
Identifiers
Local EPrints ID: 480900
URI: http://eprints.soton.ac.uk/id/eprint/480900
ISSN: 0942-4962
PURE UUID: 8a111367-de1a-4647-8b6a-98038b90bdd9
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Date deposited: 10 Aug 2023 16:51
Last modified: 17 Mar 2024 01:12
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Contributors
Author:
Reza Moradi Rad
Author:
Abdolrahman Attar
Author:
Asadollah Shahbahrami
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