Predictive vector quantization using neural networks

M. R. Hashemi; T. H. Yeap; S. Panchanathan

Predictive vector quantization using neural networks

M. R. Hashemi, T. H. Yeap, S. Panchanathan

Research output: Contribution to journal › Conference article › peer-review

Abstract

In this paper we propose a new predictive vector quantization (PVQ) technique for image and video compression. This technique has been implemented using neural networks. A Kohonen self-organized feature map is used to implement the vector quantizer, while a multi layer perceptron implements the predictor. The proposed technique provides a superior coding performance.

Original language	English (US)
Pages (from-to)	834-837
Number of pages	4
Journal	Canadian Conference on Electrical and Computer Engineering
Volume	2
State	Published - Dec 1 1995
Externally published	Yes
Event	Proceedings of the 1995 Canadian Conference on Electrical and Computer Engineering. Part 1 (of 2) - Montreal, Can Duration: Sep 5 1995 → Sep 8 1995

ASJC Scopus subject areas

Hardware and Architecture
Electrical and Electronic Engineering

Cite this

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title = "Predictive vector quantization using neural networks",

abstract = "In this paper we propose a new predictive vector quantization (PVQ) technique for image and video compression. This technique has been implemented using neural networks. A Kohonen self-organized feature map is used to implement the vector quantizer, while a multi layer perceptron implements the predictor. The proposed technique provides a superior coding performance.",

author = "Hashemi, {M. R.} and Yeap, {T. H.} and S. Panchanathan",

year = "1995",

month = dec,

day = "1",

language = "English (US)",

volume = "2",

pages = "834--837",

journal = "Canadian Conference on Electrical and Computer Engineering",

issn = "0840-7789",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "Proceedings of the 1995 Canadian Conference on Electrical and Computer Engineering. Part 1 (of 2) ; Conference date: 05-09-1995 Through 08-09-1995",

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T1 - Predictive vector quantization using neural networks

AU - Hashemi, M. R.

AU - Yeap, T. H.

AU - Panchanathan, S.

PY - 1995/12/1

Y1 - 1995/12/1

N2 - In this paper we propose a new predictive vector quantization (PVQ) technique for image and video compression. This technique has been implemented using neural networks. A Kohonen self-organized feature map is used to implement the vector quantizer, while a multi layer perceptron implements the predictor. The proposed technique provides a superior coding performance.

AB - In this paper we propose a new predictive vector quantization (PVQ) technique for image and video compression. This technique has been implemented using neural networks. A Kohonen self-organized feature map is used to implement the vector quantizer, while a multi layer perceptron implements the predictor. The proposed technique provides a superior coding performance.

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UR - http://www.scopus.com/inward/citedby.url?scp=0029509344&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:0029509344

SN - 0840-7789

VL - 2

SP - 834

EP - 837

JO - Canadian Conference on Electrical and Computer Engineering

JF - Canadian Conference on Electrical and Computer Engineering

T2 - Proceedings of the 1995 Canadian Conference on Electrical and Computer Engineering. Part 1 (of 2)

Y2 - 5 September 1995 through 8 September 1995

ER -

Predictive vector quantization using neural networks

Abstract

ASJC Scopus subject areas

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