Introduction to Information Theory and Data Compression

Hankerson, Darrel; Johnson, Peter; Harris, Gregory A.

doi:10.1201/9781420035278

Cited by 76 publications

(16 citation statements)

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“…This requirement is known as the admissibility condition. In a more general way, ψ(t) is replaced by (t), allowing a variety of choices, which can enhance certain features for some particular applications (Starck et al, 1998;Stromme, 1999& Hankerson et al, 2005. However, the CWT in the form defined by equation (6) is highly redundant, which makes its direct implementation of minor interest.…”

Section: Continuous Wavelet Transformmentioning

confidence: 99%

“…However, since equations (28 and 29) compute one output for each two consecutive inputs, a modification needs to be made. The basic operation required here, is derived from the multirate signal processing theory (Fliege, 1994;Hankerson et al, 2005;Cunha et al, 2006;Lu & Do, 2007;Nguyen & Oraintara, 2008& Brislawn, 2010. It simply consists of using a down-sampler or decimator by a factor of two.…”

Section: Filter Banks and Wavelet Implementationsmentioning

confidence: 99%

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The Wavelet Transform for Image Processing Applications

Bouden¹,

Mokhtar²

2012

Advances in Wavelet Theory and Their Applications in Engineering, Physics and Technology

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Section: Continuous Wavelet Transformmentioning

confidence: 99%

Section: Filter Banks and Wavelet Implementationsmentioning

confidence: 99%

The Wavelet Transform for Image Processing Applications

Bouden¹,

Mokhtar²

2012

Advances in Wavelet Theory and Their Applications in Engineering, Physics and Technology

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“…The compression model is presented in Figure 5. The encoding can be by lossless or lossy methods (Hankerson et al, 2005). First, the data in the receiver are the same that in the transmitter; while in the second, a part of the information is lost in the process.…”

Section: Compression Modelmentioning

confidence: 99%

Discrete Wavelet Transform in Compression and Filtering of Biomedical Signals

Ballesteros¹,

Gaona²,

Martínez³

2011

Discrete Wavelet Transforms - Biomedical Applications

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“…The JPEG has four distinct modes of operation [1,2,3,4,5,6]. The Lossy Sequential DCT Based Mode (or Baseline JPEG) is most popular among them since it can achieve highest compression ratio beside preserving maximum possible image quality.…”

Section: Introductionmentioning

confidence: 99%

“…However, Run-length or Arithmetic Coding can also be applied in this stage without compromising the resulting number of bits since all of them are optimal coding [1,7]. As an already optimally compressed bit stream can not further be compressed [6,10], any effort to further compression of a JPEG coded bit stream is meaningless. However, since the Baseline JPEG Default Huffman Coding results different depending on the distribution of the frequency of the input data set, if the data set of an image applied for Huffman Coding is modified, obtaining more optimum result is still possible.…”

Section: Introductionmentioning

confidence: 99%

An Improved JPEG Image Compression Technique based on Selective Quantization

Hasan¹,

Nur²,

Shakur³

2012

IJCA

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In today's communicative and multimedia computing world, JPEG images play a vast consequential role. The JPEG images have been able to satisfy the users by fulfilling their demand of preserving numerous digital images within considerably less storage space. Although the JPEG standard offers four different sorts of compression mechanism, among them the Baseline JPEG or Lossy Sequential DCT Mode of JPEG is most popular since it can store a digital image by temporarily removing its psychovisual redundancy and thereby offering a very less storage space for a large image. Again, the computational complexity of Baseline JPEG is also considerably less as compression takes place in Discrete Cosine Transform domain. Therefore, Baseline JPEG is substantially useful while storing, sharing and transmitting digital images. Despite removing a large amount of psychovisual redundancy, the Baseline JPEG still contains redundant data in DCT domain. This paper explores the fact and introduces an improved technique that modifies the Baseline JPEG algorithm. It describes a way to further compress a JPEG image without any additional loss while achieving a better compression ratio than that is achievable by Baseline JPEG. The contribution of this work is to incorporate a simple mathematical series with Baseline JPEG before applying optimal encoding and perform a Selective Quantization that essentially does not loss any information after decompression but reduces the redundant data in DCT domain. The proposed technique is tested on over 200 textbook images that are extensively used for testing standard Image Processing and Computer Vision algorithms. The experimental results show that our proposed approach achieves 2.15% and 14.10% better compression ratio than that is achieved by Baseline JPEG on an average for gray-scale and true-color images respectively. General Terms:JPEG, Huffman Coding

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Introduction to Information Theory and Data Compression

Cited by 76 publications

References 0 publications

The Wavelet Transform for Image Processing Applications

The Wavelet Transform for Image Processing Applications

Discrete Wavelet Transform in Compression and Filtering of Biomedical Signals

An Improved JPEG Image Compression Technique based on Selective Quantization

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