The Impact of State-of-the-Art Techniques for Lossless Still Image Compression

A great deal of information is produced daily, due to advances in telecommunication, and the issue of storing it on digital devices or transmitting it over the Internet is challenging. Data compression is essential in managing this information well. Therefore, research on data compression has become...
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Autor*in:	Md. Atiqur Rahman [verfasserIn] Mohamed Hamada [verfasserIn] Jungpil Shin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	image compression lossless JPEG JPEG 2000 PNG JPGE-LS JPEG XR

Übergeordnetes Werk:	In: Electronics - MDPI AG, 2013, 10(2021), 3, p 360
Übergeordnetes Werk:	volume:10 ; year:2021 ; number:3, p 360

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/electronics10030360

Katalog-ID:	DOAJ031051391

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spelling	10.3390/electronics10030360 doi (DE-627)DOAJ031051391 (DE-599)DOAJ703a706edb4046a78f6c764e2bab0f81 DE-627 ger DE-627 rakwb eng TK7800-8360 Md. Atiqur Rahman verfasserin aut The Impact of State-of-the-Art Techniques for Lossless Still Image Compression 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A great deal of information is produced daily, due to advances in telecommunication, and the issue of storing it on digital devices or transmitting it over the Internet is challenging. Data compression is essential in managing this information well. Therefore, research on data compression has become a topic of great interest to researchers, and the number of applications in this area is increasing. Over the last few decades, international organisations have developed many strategies for data compression, and there is no specific algorithm that works well on all types of data. The compression ratio, as well as encoding and decoding times, are mainly used to evaluate an algorithm for lossless image compression. However, although the compression ratio is more significant for some applications, others may require higher encoding or decoding speeds or both; alternatively, all three parameters may be equally important. The main aim of this article is to analyse the most advanced lossless image compression algorithms from each point of view, and evaluate the strength of each algorithm for each kind of image. We develop a technique regarding how to evaluate an image compression algorithm that is based on more than one parameter. The findings that are presented in this paper may be helpful to new researchers and to users in this area. image compression lossless JPEG JPEG 2000 PNG JPGE-LS JPEG XR Electronics Mohamed Hamada verfasserin aut Jungpil Shin verfasserin aut In Electronics MDPI AG, 2013 10(2021), 3, p 360 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:10 year:2021 number:3, p 360 https://doi.org/10.3390/electronics10030360 kostenfrei https://doaj.org/article/703a706edb4046a78f6c764e2bab0f81 kostenfrei https://www.mdpi.com/2079-9292/10/3/360 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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 10 2021 3, p 360
allfields_unstemmed	10.3390/electronics10030360 doi (DE-627)DOAJ031051391 (DE-599)DOAJ703a706edb4046a78f6c764e2bab0f81 DE-627 ger DE-627 rakwb eng TK7800-8360 Md. Atiqur Rahman verfasserin aut The Impact of State-of-the-Art Techniques for Lossless Still Image Compression 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A great deal of information is produced daily, due to advances in telecommunication, and the issue of storing it on digital devices or transmitting it over the Internet is challenging. Data compression is essential in managing this information well. Therefore, research on data compression has become a topic of great interest to researchers, and the number of applications in this area is increasing. Over the last few decades, international organisations have developed many strategies for data compression, and there is no specific algorithm that works well on all types of data. The compression ratio, as well as encoding and decoding times, are mainly used to evaluate an algorithm for lossless image compression. However, although the compression ratio is more significant for some applications, others may require higher encoding or decoding speeds or both; alternatively, all three parameters may be equally important. The main aim of this article is to analyse the most advanced lossless image compression algorithms from each point of view, and evaluate the strength of each algorithm for each kind of image. We develop a technique regarding how to evaluate an image compression algorithm that is based on more than one parameter. The findings that are presented in this paper may be helpful to new researchers and to users in this area. image compression lossless JPEG JPEG 2000 PNG JPGE-LS JPEG XR Electronics Mohamed Hamada verfasserin aut Jungpil Shin verfasserin aut In Electronics MDPI AG, 2013 10(2021), 3, p 360 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:10 year:2021 number:3, p 360 https://doi.org/10.3390/electronics10030360 kostenfrei https://doaj.org/article/703a706edb4046a78f6c764e2bab0f81 kostenfrei https://www.mdpi.com/2079-9292/10/3/360 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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 10 2021 3, p 360
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allfieldsSound	10.3390/electronics10030360 doi (DE-627)DOAJ031051391 (DE-599)DOAJ703a706edb4046a78f6c764e2bab0f81 DE-627 ger DE-627 rakwb eng TK7800-8360 Md. Atiqur Rahman verfasserin aut The Impact of State-of-the-Art Techniques for Lossless Still Image Compression 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A great deal of information is produced daily, due to advances in telecommunication, and the issue of storing it on digital devices or transmitting it over the Internet is challenging. Data compression is essential in managing this information well. Therefore, research on data compression has become a topic of great interest to researchers, and the number of applications in this area is increasing. Over the last few decades, international organisations have developed many strategies for data compression, and there is no specific algorithm that works well on all types of data. The compression ratio, as well as encoding and decoding times, are mainly used to evaluate an algorithm for lossless image compression. However, although the compression ratio is more significant for some applications, others may require higher encoding or decoding speeds or both; alternatively, all three parameters may be equally important. The main aim of this article is to analyse the most advanced lossless image compression algorithms from each point of view, and evaluate the strength of each algorithm for each kind of image. We develop a technique regarding how to evaluate an image compression algorithm that is based on more than one parameter. The findings that are presented in this paper may be helpful to new researchers and to users in this area. image compression lossless JPEG JPEG 2000 PNG JPGE-LS JPEG XR Electronics Mohamed Hamada verfasserin aut Jungpil Shin verfasserin aut In Electronics MDPI AG, 2013 10(2021), 3, p 360 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:10 year:2021 number:3, p 360 https://doi.org/10.3390/electronics10030360 kostenfrei https://doaj.org/article/703a706edb4046a78f6c764e2bab0f81 kostenfrei https://www.mdpi.com/2079-9292/10/3/360 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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 10 2021 3, p 360
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authorswithroles_txt_mv	Md. Atiqur Rahman @@aut@@ Mohamed Hamada @@aut@@ Jungpil Shin @@aut@@
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callnumber-first	T - Technology
author	Md. Atiqur Rahman
spellingShingle	Md. Atiqur Rahman misc TK7800-8360 misc image compression misc lossless JPEG misc JPEG 2000 misc PNG misc JPGE-LS misc JPEG XR misc Electronics The Impact of State-of-the-Art Techniques for Lossless Still Image Compression
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title	The Impact of State-of-the-Art Techniques for Lossless Still Image Compression
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title_auth	The Impact of State-of-the-Art Techniques for Lossless Still Image Compression
abstract	A great deal of information is produced daily, due to advances in telecommunication, and the issue of storing it on digital devices or transmitting it over the Internet is challenging. Data compression is essential in managing this information well. Therefore, research on data compression has become a topic of great interest to researchers, and the number of applications in this area is increasing. Over the last few decades, international organisations have developed many strategies for data compression, and there is no specific algorithm that works well on all types of data. The compression ratio, as well as encoding and decoding times, are mainly used to evaluate an algorithm for lossless image compression. However, although the compression ratio is more significant for some applications, others may require higher encoding or decoding speeds or both; alternatively, all three parameters may be equally important. The main aim of this article is to analyse the most advanced lossless image compression algorithms from each point of view, and evaluate the strength of each algorithm for each kind of image. We develop a technique regarding how to evaluate an image compression algorithm that is based on more than one parameter. The findings that are presented in this paper may be helpful to new researchers and to users in this area.
abstractGer	A great deal of information is produced daily, due to advances in telecommunication, and the issue of storing it on digital devices or transmitting it over the Internet is challenging. Data compression is essential in managing this information well. Therefore, research on data compression has become a topic of great interest to researchers, and the number of applications in this area is increasing. Over the last few decades, international organisations have developed many strategies for data compression, and there is no specific algorithm that works well on all types of data. The compression ratio, as well as encoding and decoding times, are mainly used to evaluate an algorithm for lossless image compression. However, although the compression ratio is more significant for some applications, others may require higher encoding or decoding speeds or both; alternatively, all three parameters may be equally important. The main aim of this article is to analyse the most advanced lossless image compression algorithms from each point of view, and evaluate the strength of each algorithm for each kind of image. We develop a technique regarding how to evaluate an image compression algorithm that is based on more than one parameter. The findings that are presented in this paper may be helpful to new researchers and to users in this area.
abstract_unstemmed	A great deal of information is produced daily, due to advances in telecommunication, and the issue of storing it on digital devices or transmitting it over the Internet is challenging. Data compression is essential in managing this information well. Therefore, research on data compression has become a topic of great interest to researchers, and the number of applications in this area is increasing. Over the last few decades, international organisations have developed many strategies for data compression, and there is no specific algorithm that works well on all types of data. The compression ratio, as well as encoding and decoding times, are mainly used to evaluate an algorithm for lossless image compression. However, although the compression ratio is more significant for some applications, others may require higher encoding or decoding speeds or both; alternatively, all three parameters may be equally important. The main aim of this article is to analyse the most advanced lossless image compression algorithms from each point of view, and evaluate the strength of each algorithm for each kind of image. We develop a technique regarding how to evaluate an image compression algorithm that is based on more than one parameter. The findings that are presented in this paper may be helpful to new researchers and to users in this area.
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