PCBMS: A Model to Select an Optimal Lossless Image Compression Technique

This article presents a parameter combination-based method selection (PCBMS) approach to select an optimal lossless data compression technique and provides an analysis based on experimental results to show its effectiveness. There are different types of data such as image, audio, video, and text. Th...
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Autor*in:	Md. Atiqur Rahman [verfasserIn] Mohamed Hamada [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Data compression compression ratio bits per pixel encoding and decoding times

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 9(2021), Seite 167426-167433
Übergeordnetes Werk:	volume:9 ; year:2021 ; pages:167426-167433

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2021.3137345

Katalog-ID:	DOAJ078688213

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spelling	10.1109/ACCESS.2021.3137345 doi (DE-627)DOAJ078688213 (DE-599)DOAJb740665c8e104342a8c2cbc81fbfc66f DE-627 ger DE-627 rakwb eng TK1-9971 Md. Atiqur Rahman verfasserin aut PCBMS: A Model to Select an Optimal Lossless Image Compression Technique 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This article presents a parameter combination-based method selection (PCBMS) approach to select an optimal lossless data compression technique and provides an analysis based on experimental results to show its effectiveness. There are different types of data such as image, audio, video, and text. These data are classified based on the number of bits. Many algorithms have been developed to compress data over the past few decades, but no developed algorithm works well on all types of data. Lossless data compression techniques are mainly evaluated based on the compression ratio, encoding, and decoding time. While a higher compression ratio is more important for some applications, others may require faster encoding or decoding, or both. Alternatively, each of the three parameters can be equally significant. Choosing an optimal algorithm from many algorithms based on an application’s requirements is a significant challenge. By analyzing the data from each perspective, this model recommends an algorithm as the best for each type of data. Based on the proposed model, an analysis is provided. For some sets of data, it has been demonstrated that the proposed method gives a better prediction to select an algorithm according to the needs of an application. Data compression compression ratio bits per pixel encoding and decoding times Electrical engineering. Electronics. Nuclear engineering Mohamed Hamada verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 167426-167433 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:167426-167433 https://doi.org/10.1109/ACCESS.2021.3137345 kostenfrei https://doaj.org/article/b740665c8e104342a8c2cbc81fbfc66f kostenfrei https://ieeexplore.ieee.org/document/9656929/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 167426-167433
allfields_unstemmed	10.1109/ACCESS.2021.3137345 doi (DE-627)DOAJ078688213 (DE-599)DOAJb740665c8e104342a8c2cbc81fbfc66f DE-627 ger DE-627 rakwb eng TK1-9971 Md. Atiqur Rahman verfasserin aut PCBMS: A Model to Select an Optimal Lossless Image Compression Technique 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This article presents a parameter combination-based method selection (PCBMS) approach to select an optimal lossless data compression technique and provides an analysis based on experimental results to show its effectiveness. There are different types of data such as image, audio, video, and text. These data are classified based on the number of bits. Many algorithms have been developed to compress data over the past few decades, but no developed algorithm works well on all types of data. Lossless data compression techniques are mainly evaluated based on the compression ratio, encoding, and decoding time. While a higher compression ratio is more important for some applications, others may require faster encoding or decoding, or both. Alternatively, each of the three parameters can be equally significant. Choosing an optimal algorithm from many algorithms based on an application’s requirements is a significant challenge. By analyzing the data from each perspective, this model recommends an algorithm as the best for each type of data. Based on the proposed model, an analysis is provided. For some sets of data, it has been demonstrated that the proposed method gives a better prediction to select an algorithm according to the needs of an application. Data compression compression ratio bits per pixel encoding and decoding times Electrical engineering. Electronics. Nuclear engineering Mohamed Hamada verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 167426-167433 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:167426-167433 https://doi.org/10.1109/ACCESS.2021.3137345 kostenfrei https://doaj.org/article/b740665c8e104342a8c2cbc81fbfc66f kostenfrei https://ieeexplore.ieee.org/document/9656929/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 167426-167433
allfieldsGer	10.1109/ACCESS.2021.3137345 doi (DE-627)DOAJ078688213 (DE-599)DOAJb740665c8e104342a8c2cbc81fbfc66f DE-627 ger DE-627 rakwb eng TK1-9971 Md. Atiqur Rahman verfasserin aut PCBMS: A Model to Select an Optimal Lossless Image Compression Technique 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This article presents a parameter combination-based method selection (PCBMS) approach to select an optimal lossless data compression technique and provides an analysis based on experimental results to show its effectiveness. There are different types of data such as image, audio, video, and text. These data are classified based on the number of bits. Many algorithms have been developed to compress data over the past few decades, but no developed algorithm works well on all types of data. Lossless data compression techniques are mainly evaluated based on the compression ratio, encoding, and decoding time. While a higher compression ratio is more important for some applications, others may require faster encoding or decoding, or both. Alternatively, each of the three parameters can be equally significant. Choosing an optimal algorithm from many algorithms based on an application’s requirements is a significant challenge. By analyzing the data from each perspective, this model recommends an algorithm as the best for each type of data. Based on the proposed model, an analysis is provided. For some sets of data, it has been demonstrated that the proposed method gives a better prediction to select an algorithm according to the needs of an application. Data compression compression ratio bits per pixel encoding and decoding times Electrical engineering. Electronics. Nuclear engineering Mohamed Hamada verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 167426-167433 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:167426-167433 https://doi.org/10.1109/ACCESS.2021.3137345 kostenfrei https://doaj.org/article/b740665c8e104342a8c2cbc81fbfc66f kostenfrei https://ieeexplore.ieee.org/document/9656929/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 167426-167433
allfieldsSound	10.1109/ACCESS.2021.3137345 doi (DE-627)DOAJ078688213 (DE-599)DOAJb740665c8e104342a8c2cbc81fbfc66f DE-627 ger DE-627 rakwb eng TK1-9971 Md. Atiqur Rahman verfasserin aut PCBMS: A Model to Select an Optimal Lossless Image Compression Technique 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This article presents a parameter combination-based method selection (PCBMS) approach to select an optimal lossless data compression technique and provides an analysis based on experimental results to show its effectiveness. There are different types of data such as image, audio, video, and text. These data are classified based on the number of bits. Many algorithms have been developed to compress data over the past few decades, but no developed algorithm works well on all types of data. Lossless data compression techniques are mainly evaluated based on the compression ratio, encoding, and decoding time. While a higher compression ratio is more important for some applications, others may require faster encoding or decoding, or both. Alternatively, each of the three parameters can be equally significant. Choosing an optimal algorithm from many algorithms based on an application’s requirements is a significant challenge. By analyzing the data from each perspective, this model recommends an algorithm as the best for each type of data. Based on the proposed model, an analysis is provided. For some sets of data, it has been demonstrated that the proposed method gives a better prediction to select an algorithm according to the needs of an application. Data compression compression ratio bits per pixel encoding and decoding times Electrical engineering. Electronics. Nuclear engineering Mohamed Hamada verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 167426-167433 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:167426-167433 https://doi.org/10.1109/ACCESS.2021.3137345 kostenfrei https://doaj.org/article/b740665c8e104342a8c2cbc81fbfc66f kostenfrei https://ieeexplore.ieee.org/document/9656929/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 167426-167433
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topic_title	TK1-9971 PCBMS: A Model to Select an Optimal Lossless Image Compression Technique Data compression compression ratio bits per pixel encoding and decoding times
topic	misc TK1-9971 misc Data compression misc compression ratio misc bits per pixel misc encoding and decoding times misc Electrical engineering. Electronics. Nuclear engineering
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title	PCBMS: A Model to Select an Optimal Lossless Image Compression Technique
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abstract	This article presents a parameter combination-based method selection (PCBMS) approach to select an optimal lossless data compression technique and provides an analysis based on experimental results to show its effectiveness. There are different types of data such as image, audio, video, and text. These data are classified based on the number of bits. Many algorithms have been developed to compress data over the past few decades, but no developed algorithm works well on all types of data. Lossless data compression techniques are mainly evaluated based on the compression ratio, encoding, and decoding time. While a higher compression ratio is more important for some applications, others may require faster encoding or decoding, or both. Alternatively, each of the three parameters can be equally significant. Choosing an optimal algorithm from many algorithms based on an application’s requirements is a significant challenge. By analyzing the data from each perspective, this model recommends an algorithm as the best for each type of data. Based on the proposed model, an analysis is provided. For some sets of data, it has been demonstrated that the proposed method gives a better prediction to select an algorithm according to the needs of an application.
abstractGer	This article presents a parameter combination-based method selection (PCBMS) approach to select an optimal lossless data compression technique and provides an analysis based on experimental results to show its effectiveness. There are different types of data such as image, audio, video, and text. These data are classified based on the number of bits. Many algorithms have been developed to compress data over the past few decades, but no developed algorithm works well on all types of data. Lossless data compression techniques are mainly evaluated based on the compression ratio, encoding, and decoding time. While a higher compression ratio is more important for some applications, others may require faster encoding or decoding, or both. Alternatively, each of the three parameters can be equally significant. Choosing an optimal algorithm from many algorithms based on an application’s requirements is a significant challenge. By analyzing the data from each perspective, this model recommends an algorithm as the best for each type of data. Based on the proposed model, an analysis is provided. For some sets of data, it has been demonstrated that the proposed method gives a better prediction to select an algorithm according to the needs of an application.
abstract_unstemmed	This article presents a parameter combination-based method selection (PCBMS) approach to select an optimal lossless data compression technique and provides an analysis based on experimental results to show its effectiveness. There are different types of data such as image, audio, video, and text. These data are classified based on the number of bits. Many algorithms have been developed to compress data over the past few decades, but no developed algorithm works well on all types of data. Lossless data compression techniques are mainly evaluated based on the compression ratio, encoding, and decoding time. While a higher compression ratio is more important for some applications, others may require faster encoding or decoding, or both. Alternatively, each of the three parameters can be equally significant. Choosing an optimal algorithm from many algorithms based on an application’s requirements is a significant challenge. By analyzing the data from each perspective, this model recommends an algorithm as the best for each type of data. Based on the proposed model, an analysis is provided. For some sets of data, it has been demonstrated that the proposed method gives a better prediction to select an algorithm according to the needs of an application.
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title_short	PCBMS: A Model to Select an Optimal Lossless Image Compression Technique
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