Computational Paradigm and Quantitative Optimization to Parallel Processing Performance of Still Image Compression

Fashionable and staggering evolution in inferring the parallel processing routine coupled with the necessity to amass and distribute huge magnitude of digital records especially still images has fetched an amount of confronts for researchers and other stakeholders. These disputes exorbitantly outlay and maneuvers the digital information among others, subsists the spotlight of the research civilization in topical days and encompasses the lead to the exploration of image compression methods that can accomplish exceptional outcomes. One of those practices is the parallel processing of a diversity of compression techniques, which facilitates split, an image into ingredients of reverse occurrences and has the benefit of great compression. This manuscript scrutinizes the computational intricacy and the quantitative optimization of diverse still image compression tactics and additional accede to the recital of parallel processing. The computational efficacy is analyzed and estimated with respect to the Central Processing Unit (CPU) as well as Graphical Processing Unit (GPU). The PSNR (Peak Signal to Noise Ratio) is exercised to guesstimate image re-enactment and eminence in harmonization. The moments are obtained and conferred with support on different still image compression algorithms such as Block Truncation Coding (BTC), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Dual Tree Complex Wavelet Transform (DTCWT), Set Partitioning in Hierarchical Trees (SPIHT), Embedded Zero-tree Wavelet (EZW). The evaluation is conceded in provisos of coding efficacy, memory constraints, image quantity and quality.

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A Unified Scheme For Fast And Efficient Spiht Coding Algorithm Used For Image Compression

SAMRIDDHI A Journal of Physical Sciences Engineering and Technology ◽

10.18090/samriddhi.v4i2.1507 ◽

2015 ◽

Vol 4 (2) ◽

Author(s):

Amir Athar Khan ◽

Amanat Ali ◽

Sanawar Alam ◽

N. R. Kidwai

Keyword(s):

Wavelet Transform ◽

Image Compression ◽

Discrete Wavelet Transform ◽

Set Partitioning ◽

Discrete Wavelet ◽

Hierarchical Trees ◽

Different Types ◽

Spiht Coding

This paper concerns Image compression obtained with wavelet-based compression techniques such as set–partitioning in hierarchical trees (SPIHT)yield very good results The necessity in image compression continuously grows during the last decade, different types of methods is used for this mainly EZW, SPIHT and others. In this paper we used discrete wavelet transform and after this set-partitioning in hierarchical trees (SPIHT) with some improvement in respect of encoding and decoding time with better PSNR with respect to EZW coding.

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An Efficient Lossless ROI Image Compression Using Wavelet-Based Modified Region Growing Algorithm

Journal of Intelligent Systems ◽

10.1515/jisys-2018-0180 ◽

2018 ◽

Vol 29 (1) ◽

pp. 1063-1078

Author(s):

P. Sreenivasulu ◽

S. Varadarajan

Keyword(s):

Wavelet Transform ◽

Image Compression ◽

Medical Image ◽

Medical Images ◽

Region Of Interest ◽

Region Growing ◽

Set Partitioning ◽

Discrete Wavelet ◽

Huffman Encoding ◽

Encoding Method

Abstract Nowadays, medical imaging and telemedicine are increasingly being utilized on a huge scale. The expanding interest in storing and sending medical images brings a lack of adequate memory spaces and transmission bandwidth. To resolve these issues, compression was introduced. The main aim of lossless image compression is to improve accuracy, reduce the bit rate, and improve the compression efficiency for the storage and transmission of medical images while maintaining an acceptable image quality for diagnosis purposes. In this paper, we propose lossless medical image compression using wavelet transform and encoding method. Basically, the proposed image compression system comprises three modules: (i) segmentation, (ii) image compression, and (iii) image decompression. First, the input medical image is segmented into region of interest (ROI) and non-ROI using a modified region growing algorithm. Subsequently, the ROI is compressed by discrete cosine transform and set partitioning in hierarchical tree encoding method, and the non-ROI is compressed by discrete wavelet transform and merging-based Huffman encoding method. Finally, the compressed image combination of the compressed ROI and non-ROI is obtained. Then, in the decompression stage, the original medical image is extracted using the reverse procedure. The experimentation was carried out using different medical images, and the proposed method obtained better results compared to different other methods.

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Characteristics analysis of wavelet coefficients and its applications in image compression

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691314500283 ◽

2014 ◽

Vol 12 (03) ◽

pp. 1450028 ◽

Cited By ~ 2

Author(s):

Fangnian Lang ◽

Jiliu Zhou ◽

Yuan Yan Tang ◽

Hongnian Yu ◽

Shuang Cang ◽

...

Keyword(s):

Wavelet Transform ◽

Image Compression ◽

Wavelet Coefficient ◽

Correlation Property ◽

Discrete Wavelet ◽

Wavelet Coefficients ◽

Wavelet Bases ◽

Discrete Form ◽

Still Image ◽

Characteristics Analysis

Currently, the wavelet transform is widely used in the signal processing domain, especially in the image compression because of its excellent de-correlation property and the redundancy property included in the wavelet coefficients. This paper investigates the redundancy relationships between any two or three components of the wavelet coefficients, the wavelet bases and the original signal. We discuss those contents for every condition according to the continuous form and the discrete form, respectively, by which we also derive a uniform formula which illuminates the inherent connection among the redundancy of the wavelet coefficients, the wavelet bases and the original signals. Finally, we present the application of the wavelet coefficient redundancy property in the still image compression domain and compare the properties of the Discrete Wavelet Transform (DWT) with that of the Discrete Cosine Transform (DCT).

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Notice of Retraction Effects vanishing moments of discrete wavelet transform on MRI image compression algorithms

APTIKOM Journal on Computer Science and Information Technologies ◽

10.11591/aptikom.j.csit.86 ◽

2019 ◽

Vol 3 (2) ◽

pp. 47-50

Author(s):

R. Pandian ◽

S. LalithaKumari

Keyword(s):

Wavelet Transform ◽

Image Compression ◽

Discrete Wavelet Transform ◽

Image Data ◽

Set Partitioning ◽

Compression Algorithm ◽

Discrete Wavelet ◽

Expert Committee ◽

Compression Technique ◽

Vanishing Moments

Notice of Retraction-----------------------------------------------------------------------After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of APTIKOM's Publication Principles.We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.The presenting author of this paper has the option to appeal this decision by contacting ij.aptikom@gmail.com.-----------------------------------------------------------------------Image data usually contain considerable quantity of data that is redundant and much irrelevant, whereas an image compression technique overcomes this by compressing the amount of data required to represent the image. In this work, Discrete Wavelet Transform based image compression algorithm is implemented for decomposing the image. The various encoding schemes such as Embedded Zero wavelet, (EZW), Set Partitioning In Hierarchical Trees(SPIHT) and Spatial orientation Tree Wavelet(STW) are used and their performances in the compression is evaluated and also the effectiveness of different wavelets with various vanishing moments are analyzed based on the values of PSNR, Compression ratio, Means square error and bits per pixel. The optimum compression algorithm is also found based on the results.

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An Efficient Pass Parallel SPIHT based Image Compression using Double Density Dual Tree Complex Wavelet Transform for WSN

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.l2564.1081219 ◽

2019 ◽

Vol 8 (12) ◽

pp. 2762-2768

Keyword(s):

Wavelet Transform ◽

Image Quality ◽

Image Compression ◽

Wavelet Transforms ◽

Computation Time ◽

Set Partitioning ◽

Complex Wavelet Transform ◽

Compression Technique ◽

Compression Algorithms ◽

Complex Wavelet

For the past two decades, wavelet based image compression algorithms for Wireless Sensor Network (WSN) has gained broad attention than that of the spatial based image compression algorithms. In that, Dual Tree Complex Wavelet Transforms (DTCWT) has provided better results in terms of image quality and high compression rate. However, the selection of DTCWT based image compressions for various WSN based applications is not practically suitable, due to the major limitations of WSN such as, low bandwidth, low energy consumption and storage space. Therefore, an attempt has been made in this paper to develop image compression through simulation by considering the modified block based pass parallel Set Partitioning In Hierarchical Trees (SPIHT) with Double Density Dual Tree Complex Wavelet Transform (DDDTCWT) for compressing the WSN based images. In addition, bivariate shrink method is also adopted with the DDDTCWT to obtain better image quality within less computation time. It is observed through simulation results that above mentioned proposed technique provides better performance than that of existing compression technique

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