Huffman Image Compression Incorporating DPCM and DWT

Abo‐Zahhad, Mohammed; Gharieb, R.R.; Ahmed, Sabah M.; Abd-Ellah, Mahmoud Khaled

doi:10.4236/jsip.2015.62012

Cited by 18 publications

(16 citation statements)

References 6 publications

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“…In this stage, thirteen features are taken from co-occurrence matrices which are calculated for each input image. Some of these features are described below [18]:…”

Section: 33mentioning

confidence: 99%

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Breast Cancer Diagnostic System Based on MR images Using KPCA-Wavelet Transform and Support Vector Machine

Al-Dabagh¹,

Almukhtar²

2017

IJAERS

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“…In this stage, thirteen features are taken from co-occurrence matrices which are calculated for each input image. Some of these features are described below [18]:…”

Section: 33mentioning

confidence: 99%

“…It is aimed to split the input image to the four sun-bands (LL, LH, HL, and HH). The mathematical expression of DWT is shown in an equation below [18]:…”

Section: 31mentioning

confidence: 99%

Breast Cancer Diagnostic System Based on MR images Using KPCA-Wavelet Transform and Support Vector Machine

Al-Dabagh¹,

Almukhtar²

2017

IJAERS

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“…A medical image compression approach was presented by Abo-Zahhad et al [17]. In pre-processing, the image is passed through DPCM.…”

Section: Fig 3: Block Diagram Of General Video Coding Scheme Recent mentioning

confidence: 99%

Review on Image and Video Compression Standards

2017

Asian J Pharm Clin Res

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Nowadays, the number of photos taken each day is growing exponentially on phones and the number of photos uploading on Internet is also increasing rapidly. This explosion of photos in Internet and personal devices such as phones posed a challenge to the effective storage and transmission. Multimedia files are the files having text, images, audio, video, and animations, which are large and require lots of hard disk space. Hence, these files take more time to move from one place to another place over the Internet. Image compression is an effective way to reduce the storage space and speedup the transmission. Data compression is used everywhere on the internet, that is, the videos, the images, and the music in online. Even though many different image compression schemes exist, current needs and applications require fast compression algorithms which produce acceptable quality images or video with minimum size. In this paper, image and video compression standards are discussed.

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“…Since the whole energy of the image is carried by only a few wavelet (DWT) coefficients, the IBQAC is used to encode only the coarse (low pass) wavelet coefficients [12].Some researchers describe prediction-based lossless compression [1,3,[19][20][21][22][23][24]. Moreover, the combination of wavelet transform and the concept of prediction are presented in some studies [25,26]. In [25], the image is pre-processed by DPCM and then the wavelet transform is applied to the output of the DPCM.…”

mentioning

confidence: 99%

“…Moreover, the combination of wavelet transform and the concept of prediction are presented in some studies [25,26]. In [25], the image is pre-processed by DPCM and then the wavelet transform is applied to the output of the DPCM. In [26], the image pixels are predicted by a hierarchical prediction scheme and then the wavelet transform is applied to the prediction error.…”

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confidence: 99%

Edge-Based and Prediction-Based Transformations for Lossless Image Compression

Kabir

Mondal

2018

J. Imaging

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Pixelated images are used to transmit data between computing devices that have cameras and screens. Significant compression of pixelated images has been achieved by an "edge-based transformation and entropy coding" (ETEC) algorithm recently proposed by the authors of this paper. The study of ETEC is extended in this paper with a comprehensive performance evaluation. Furthermore, a novel algorithm termed "prediction-based transformation and entropy coding" (PTEC) is proposed in this paper for pixelated images. In the first stage of the PTEC method, the image is divided hierarchically to predict the current pixel using neighboring pixels. In the second stage, the prediction errors are used to form two matrices, where one matrix contains the absolute error value and the other contains the polarity of the prediction error. Finally, entropy coding is applied to the generated matrices. This paper also compares the novel ETEC and PTEC schemes with the existing lossless compression techniques: "joint photographic experts group lossless" (JPEG-LS), "set partitioning in hierarchical trees" (SPIHT) and "differential pulse code modulation" (DPCM). Our results show that, for pixelated images, the new ETEC and PTEC algorithms provide better compression than other schemes. Results also show that PTEC has a lower compression ratio but better computation time than ETEC. Furthermore, when both compression ratio and computation time are taken into consideration, PTEC is more suitable than ETEC for compressing pixelated as well as non-pixelated images.compression are some of the wavelet-based compressions such as embedded zerotrees of wavelet transforms (EZW), joint photographic experts group (JPEG) and the moving picture experts group (MPEG) compression.A large number of research papers report image compression algorithms. For example, one study [13] is about discrete cosine transform (DCT)-based lossless image compression where the higher energy coefficients in each block are quantized. Next, an inverse DCT is performed only on the quantized coefficients. The resultant pixel values are in the 2-D spatial domain. The pixel values of two neighboring regions are then subtracted to obtain residual error sequence. The error sequence is encoded by an entropy coder such as Arithmetic or Huffman coding [13]. Image compression in the frequency domain using wavelets is reported in several studies [12,[14][15][16][17]]. In the method described in [14] lifting-based bi-orthogonal wavelet transform is used which produces coefficients that can be rounded without any loss of data. In the work of [18] wavelet transform limits the image energy within fewer coefficients which are encoded by "set partitioning in hierarchical trees" (SPIHT) algorithm.In [19] JPEG lossless (JPEG-LS), a prediction-based lossless scheme, is proposed for continuous tone images. In [14] embedded zero tree coding (EZW) method is proposed based on the zero tree hypothesis. The study in [12] proposes a compression algorithm based on combination of discrete wavelet transform ...

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Huffman Image Compression Incorporating DPCM and DWT

Abstract: This paper presents a medical image compression approach. In this approach, first the image is pre-processed by Differential Pulse Code Modulator (DPCM), second, the output of the DPCM is wavelet transformed, and finally the Huffman encoding is applied to the resulting coefficients.

Cited by 18 publications

References 6 publications

Breast Cancer Diagnostic System Based on MR images Using KPCA-Wavelet Transform and Support Vector Machine

Breast Cancer Diagnostic System Based on MR images Using KPCA-Wavelet Transform and Support Vector Machine

Review on Image and Video Compression Standards

Edge-Based and Prediction-Based Transformations for Lossless Image Compression

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