Image processing using DCT and wavelet transform

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The DCT works by separating images into parts of differing frequencies. During a step called quantization, where part of compression actually occurs, the less important frequencies are discarded. Only the most important frequencies that remain are used to retrieve the image in the decompression process. It is similar to the discrete Fourier transform where it transforms a signal or image from spatial domain to frequency domain. Wavelet transform on the other hand is a multi-resolution transform that allows a form of time-frequency analysis. It provides a progressive encoding of the image at various scales, which is more flexible. The wavelets comprise a normalized set of orthogonal functions on which the image is projected. The aim of this project is to compare the performance of the DCT and the wavelet transform in image processing. Most images contain some amount of redundancy that can sometimes be removed when the image is stored and replaced when it is reconstructed, but eliminating this redundancy does not lead to high compression. Fortunately, the human eye is not very sensitive to a wide variety of information loss. An image can be changed in many ways that are either not detectable by the human eye or do not contribute to degradation of the image. Compression of an image allows the number of bits to be reduced to represent the coded image which contains a number that is smaller than the original format. This number is variable and it depends on how the image is compressed. Different types of wavelets and different stages of DCT compression ratio have been used to perform the transform of a test image. The results were analyzed with the amount of errors introduced during the compression process.

Original languageEnglish
Pages (from-to)29-35
Number of pages7
JournalOptoelectronics and Advanced Materials, Rapid Communications
Volume6
Issue number1-2
Publication statusPublished - Jan 2012

Fingerprint

Wavelet transforms
Redundancy
Image processing
Orthogonal functions
Discrete Fourier transforms
Degradation

Keywords

  • DCT
  • Image processing
  • JPEG2000
  • Wavelet

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials

Cite this

Image processing using DCT and wavelet transform. / Aisyah, Siti; Jit Singh, Mandeep Singh.

In: Optoelectronics and Advanced Materials, Rapid Communications, Vol. 6, No. 1-2, 01.2012, p. 29-35.

Research output: Contribution to journalArticle

@article{fa7de99eba864701a95bc449e8913ff7,
title = "Image processing using DCT and wavelet transform",
abstract = "The DCT works by separating images into parts of differing frequencies. During a step called quantization, where part of compression actually occurs, the less important frequencies are discarded. Only the most important frequencies that remain are used to retrieve the image in the decompression process. It is similar to the discrete Fourier transform where it transforms a signal or image from spatial domain to frequency domain. Wavelet transform on the other hand is a multi-resolution transform that allows a form of time-frequency analysis. It provides a progressive encoding of the image at various scales, which is more flexible. The wavelets comprise a normalized set of orthogonal functions on which the image is projected. The aim of this project is to compare the performance of the DCT and the wavelet transform in image processing. Most images contain some amount of redundancy that can sometimes be removed when the image is stored and replaced when it is reconstructed, but eliminating this redundancy does not lead to high compression. Fortunately, the human eye is not very sensitive to a wide variety of information loss. An image can be changed in many ways that are either not detectable by the human eye or do not contribute to degradation of the image. Compression of an image allows the number of bits to be reduced to represent the coded image which contains a number that is smaller than the original format. This number is variable and it depends on how the image is compressed. Different types of wavelets and different stages of DCT compression ratio have been used to perform the transform of a test image. The results were analyzed with the amount of errors introduced during the compression process.",
keywords = "DCT, Image processing, JPEG2000, Wavelet",
author = "Siti Aisyah and {Jit Singh}, {Mandeep Singh}",
year = "2012",
month = "1",
language = "English",
volume = "6",
pages = "29--35",
journal = "Optoelectronics and Advanced Materials, Rapid Communications",
issn = "1842-6573",
publisher = "National Institute of Optoelectronics",
number = "1-2",

}

TY - JOUR

T1 - Image processing using DCT and wavelet transform

AU - Aisyah, Siti

AU - Jit Singh, Mandeep Singh

PY - 2012/1

Y1 - 2012/1

N2 - The DCT works by separating images into parts of differing frequencies. During a step called quantization, where part of compression actually occurs, the less important frequencies are discarded. Only the most important frequencies that remain are used to retrieve the image in the decompression process. It is similar to the discrete Fourier transform where it transforms a signal or image from spatial domain to frequency domain. Wavelet transform on the other hand is a multi-resolution transform that allows a form of time-frequency analysis. It provides a progressive encoding of the image at various scales, which is more flexible. The wavelets comprise a normalized set of orthogonal functions on which the image is projected. The aim of this project is to compare the performance of the DCT and the wavelet transform in image processing. Most images contain some amount of redundancy that can sometimes be removed when the image is stored and replaced when it is reconstructed, but eliminating this redundancy does not lead to high compression. Fortunately, the human eye is not very sensitive to a wide variety of information loss. An image can be changed in many ways that are either not detectable by the human eye or do not contribute to degradation of the image. Compression of an image allows the number of bits to be reduced to represent the coded image which contains a number that is smaller than the original format. This number is variable and it depends on how the image is compressed. Different types of wavelets and different stages of DCT compression ratio have been used to perform the transform of a test image. The results were analyzed with the amount of errors introduced during the compression process.

AB - The DCT works by separating images into parts of differing frequencies. During a step called quantization, where part of compression actually occurs, the less important frequencies are discarded. Only the most important frequencies that remain are used to retrieve the image in the decompression process. It is similar to the discrete Fourier transform where it transforms a signal or image from spatial domain to frequency domain. Wavelet transform on the other hand is a multi-resolution transform that allows a form of time-frequency analysis. It provides a progressive encoding of the image at various scales, which is more flexible. The wavelets comprise a normalized set of orthogonal functions on which the image is projected. The aim of this project is to compare the performance of the DCT and the wavelet transform in image processing. Most images contain some amount of redundancy that can sometimes be removed when the image is stored and replaced when it is reconstructed, but eliminating this redundancy does not lead to high compression. Fortunately, the human eye is not very sensitive to a wide variety of information loss. An image can be changed in many ways that are either not detectable by the human eye or do not contribute to degradation of the image. Compression of an image allows the number of bits to be reduced to represent the coded image which contains a number that is smaller than the original format. This number is variable and it depends on how the image is compressed. Different types of wavelets and different stages of DCT compression ratio have been used to perform the transform of a test image. The results were analyzed with the amount of errors introduced during the compression process.

KW - DCT

KW - Image processing

KW - JPEG2000

KW - Wavelet

UR - http://www.scopus.com/inward/record.url?scp=84860137500&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84860137500&partnerID=8YFLogxK

M3 - Article

VL - 6

SP - 29

EP - 35

JO - Optoelectronics and Advanced Materials, Rapid Communications

JF - Optoelectronics and Advanced Materials, Rapid Communications

SN - 1842-6573

IS - 1-2

ER -