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33445

Image Compression Using Advanced Optimization Algorithms

Article

Last updated: 22 Jan 2023

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Tags

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Abstract

In this paper a new image compression schema that uses three-dimensional discrete cosine transform and relies on twodimensional discrete wavelet transform, for image classification, is proposed. The proposed technique utilizes a modified quantization table and a method for converting a three-dimensional image cube into a one-dimensional array, which provides better coding efficiency in the run length coding step. To ensure faster performance, this technique provides proposed parallel computation system. Several images have been used to test the proposed algorithm. Experimental results demonstrate that the proposed algorithm outperforms previous compression methods in terms of peak-signal-to-noise ratio with a lower compression bit rate.

DOI

10.21608/pserj.2017.33445

Authors

First Name

Mohamed

Last Name

Emara

MiddleName

E.

Affiliation

Electrical Engineering Department, Faculty of Engineering, Port-Said, Egypt

Email

mohamad_emara@himc.psu.edu.eg

City

-

Orcid

-

First Name

Rehab

Last Name

Abdel-Kader

MiddleName

F.

Affiliation

Electrical Engineering Department, Faculty of Engineering, Port-Said, Egypt

Email

rehabfarouk@eng.psu.edu.eg

City

-

Orcid

-

First Name

Mohamed

Last Name

Yasein

MiddleName

S.

Affiliation

Electrical Engineering Department, Faculty of Engineering, Port-Said, Egypt

Email

myasein@eng.psu.edu.eg

City

-

Orcid

-

Volume

21

Article Issue

1

Related Issue

5598

Issue Date

2017-03-01

Receive Date

2017-02-01

Publish Date

2017-03-01

Page Start

95

Page End

108

Print ISSN

1110-6603

Online ISSN

2536-9377

Link

https://pserj.journals.ekb.eg/article_33445.html

Detail API

https://pserj.journals.ekb.eg/service?article_code=33445

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10

Type

Original Article

Type Code

813

Publication Type

Journal

Publication Title

Port-Said Engineering Research Journal

Publication Link

https://pserj.journals.ekb.eg/

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-

Details

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Article

Created At

22 Jan 2023