Composite Techniques Based Color Image Compression

Abood, Zainab Ibrahim

doi:10.31026/j.eng.2017.03.06

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…DOPs' are solid owing to their significant features, which involve localization, energy compression, watermarking, signal extraction features, numerical stability, efficient data processing, and resilient data analysis [3,[21][22][23][24][25]. At the same time, the vital characteristics of majority of DOMs are not applied to large-sized images which is due to limitation in the computation of polynomials [26].…”

Section: Introductionmentioning

confidence: 99%

Performance enhancement of high order Hahn polynomials using multithreading

Mahmmod,

Flayyih,

Fakhri

et al. 2023

PLoS ONE

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Orthogonal polynomials and their moments have significant role in image processing and computer vision field. One of the polynomials is discrete Hahn polynomials (DHaPs), which are used for compression, and feature extraction. However, when the moment order becomes high, they suffer from numerical instability. This paper proposes a fast approach for computing the high orders DHaPs. This work takes advantage of the multithread for the calculation of Hahn polynomials coefficients. To take advantage of the available processing capabilities, independent calculations are divided among threads. The research provides a distribution method to achieve a more balanced processing burden among the threads. The proposed methods are tested for various values of DHaPs parameters, sizes, and different values of threads. In comparison to the unthreaded situation, the results demonstrate an improvement in the processing time which increases as the polynomial size increases, reaching its maximum of 5.8 in the case of polynomial size and order of 8000 × 8000 (matrix size). Furthermore, the trend of continuously raising the number of threads to enhance performance is inconsistent and becomes invalid at some point when the performance improvement falls below the maximum. The number of threads that achieve the highest improvement differs according to the size, being in the range of 8 to 16 threads in 1000 × 1000 matrix size, whereas at 8000 × 8000 case it ranges from 32 to 160 threads.

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Section: Introductionmentioning

confidence: 99%

Performance enhancement of high order Hahn polynomials using multithreading

Mahmmod,

Flayyih,

Fakhri

et al. 2023

PLoS ONE

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Accelerated and Improved Stabilization for High Order Moments of Racah Polynomials

Mahmmod,

Abdulhussain,

Suk

et al. 2023

IEEE Access

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Discrete Racah polynomials (DRPs) are highly efficient orthogonal polynomials and used in various scientific fields for signal representation. They find applications in disciplines like image processing and computer vision. Racah polynomials were originally introduced by Wilson and later modified by Zhu to be orthogonal on a discrete set of samples. However, when the degree of the polynomial is high, it encounters numerical instability issues. In this paper, we propose a novel algorithm called Improved Stabilization (ImSt) for computing DRP coefficients. The algorithm partitions the DRP plane into asymmetric parts based on the polynomial size and DRP parameters. We have optimized the use of stabilizing conditions in these partitions. To compute the initial values, we employ the logarithmic gamma function along with a new formula. This combination enables us to compute the initial values efficiently for a wide range of DRP parameter values and large polynomial sizes. Additionally, we have derived a symmetry relation for the case when the Racah polynomial parameters are zero (a = 0, α = 0, β = 0). This symmetry makes the Racah polynomials symmetric about the main diagonal, and we present a different algorithm for this specific scenario. We have demonstrated that the ImSt algorithm works for a broader range of parameters and higher degrees compared to existing algorithms. A comprehensive comparison between ImSt and the existing algorithms has been conducted, considering the maximum polynomial degree, computation time, restriction error analysis, and reconstruction error. The results of the comparison indicate that ImSt outperforms the existing algorithms for various values of Racah polynomial parameters.

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A Proposed Lossy Image Compression based on Multiplication Table

Mahdi

Hassan

2017

KJAR

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Lately, Internet improved in the various trends, especially, the use of the image increased due to the daily use in several scopes like social media (Facebook, Twitter, WhatsApp, etc.), connected devices (sensor, IP camera, Internet of Things (IoT) Internet of Everything (IoE), etc) and smart phone devices that users interchanged images estimated in the billions. So, images issues in internet can be summarized into two criteria, the first criteria is considered with transmit image size. The second criteria is considered with low bandwidth through transmission. This paper exhibits a methodology for image compression using an idea of multiplication Table. The suggested algorithm helpful in realizing a preferable achievement by presenting a high Compression Ratio, preserve image quality with a high PSNR, small losing in the original image and efficiently in running time.

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Composite Techniques Based Color Image Compression

Cited by 3 publications

References 4 publications

Performance enhancement of high order Hahn polynomials using multithreading

Performance enhancement of high order Hahn polynomials using multithreading

Accelerated and Improved Stabilization for High Order Moments of Racah Polynomials

A Proposed Lossy Image Compression based on Multiplication Table

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