Improved near-lossless technique using the Huffman coding for enhancing the quality of image compression
Abstract Digital data compression aims to reduce the size of digital files in line with technological development. However, most data is distinguished by its large size, which requires a large storage capacity, and requires a long time in transmission operations via the Internet. Therefore, a new co...
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| Autor*in: |
Otair, Mohammed [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 81(2022), 20 vom: 30. März, Seite 28509-28529 |
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| Übergeordnetes Werk: |
volume:81 ; year:2022 ; number:20 ; day:30 ; month:03 ; pages:28509-28529 |
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| DOI / URN: |
10.1007/s11042-022-12846-8 |
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SPR047666498 |
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| 245 | 1 | 0 | |a Improved near-lossless technique using the Huffman coding for enhancing the quality of image compression |
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| 520 | |a Abstract Digital data compression aims to reduce the size of digital files in line with technological development. However, most data is distinguished by its large size, which requires a large storage capacity, and requires a long time in transmission operations via the Internet. Therefore, a new compress files method is needed to reduce the image size, maintain its quality, utilize storage spaces, and minimize time. This paper aims to improve digital image compression’s compression rates by dividing the image into several blocks. Thus, a new near-lossless method using the Huffman Coding technique is proposed. Digital image compression techniques are classified as lossless and lossy. Huffman Coding is a lossless-based technique used in the proposed method to maintain image quality during compression. The proposed method consists of several steps, which are dividing the image into blocks, finding the lowest value in each block and subtracting it from the rest of the values in the same block, then subtracting one from the odd numbers, dividing all the values on two, and finally applying the Huffman Coding technique to the block. The proposed method is applied to a well-known gray and color set with different types and different dimensions. Standard evaluation measures are used (i.e., PSNR, MSE, and CR) to evaluate the proposed method’s performance. When compressing images using the proposed method, the results demonstrated 0.11% enhancement when used two by two blocks. It also got high compression rates (25%). | ||
| 650 | 4 | |a Near-lossless technique |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Huffman coding |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Image quality |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Image compression |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Abualigah, Laith |4 aut | |
| 700 | 1 | |a Qawaqzeh, Mohammed K. |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Multimedia tools and applications |d Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 |g 81(2022), 20 vom: 30. März, Seite 28509-28529 |w (DE-627)27135030X |w (DE-600)1479928-5 |x 1573-7721 |7 nnns |
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10.1007/s11042-022-12846-8 doi (DE-627)SPR047666498 (SPR)s11042-022-12846-8-e DE-627 ger DE-627 rakwb eng Otair, Mohammed verfasserin aut Improved near-lossless technique using the Huffman coding for enhancing the quality of image compression 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Digital data compression aims to reduce the size of digital files in line with technological development. However, most data is distinguished by its large size, which requires a large storage capacity, and requires a long time in transmission operations via the Internet. Therefore, a new compress files method is needed to reduce the image size, maintain its quality, utilize storage spaces, and minimize time. This paper aims to improve digital image compression’s compression rates by dividing the image into several blocks. Thus, a new near-lossless method using the Huffman Coding technique is proposed. Digital image compression techniques are classified as lossless and lossy. Huffman Coding is a lossless-based technique used in the proposed method to maintain image quality during compression. The proposed method consists of several steps, which are dividing the image into blocks, finding the lowest value in each block and subtracting it from the rest of the values in the same block, then subtracting one from the odd numbers, dividing all the values on two, and finally applying the Huffman Coding technique to the block. The proposed method is applied to a well-known gray and color set with different types and different dimensions. Standard evaluation measures are used (i.e., PSNR, MSE, and CR) to evaluate the proposed method’s performance. When compressing images using the proposed method, the results demonstrated 0.11% enhancement when used two by two blocks. It also got high compression rates (25%). Near-lossless technique (dpeaa)DE-He213 Huffman coding (dpeaa)DE-He213 Image quality (dpeaa)DE-He213 Image compression (dpeaa)DE-He213 Abualigah, Laith aut Qawaqzeh, Mohammed K. aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 20 vom: 30. März, Seite 28509-28529 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:20 day:30 month:03 pages:28509-28529 https://dx.doi.org/10.1007/s11042-022-12846-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81 2022 20 30 03 28509-28529 |
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10.1007/s11042-022-12846-8 doi (DE-627)SPR047666498 (SPR)s11042-022-12846-8-e DE-627 ger DE-627 rakwb eng Otair, Mohammed verfasserin aut Improved near-lossless technique using the Huffman coding for enhancing the quality of image compression 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Digital data compression aims to reduce the size of digital files in line with technological development. However, most data is distinguished by its large size, which requires a large storage capacity, and requires a long time in transmission operations via the Internet. Therefore, a new compress files method is needed to reduce the image size, maintain its quality, utilize storage spaces, and minimize time. This paper aims to improve digital image compression’s compression rates by dividing the image into several blocks. Thus, a new near-lossless method using the Huffman Coding technique is proposed. Digital image compression techniques are classified as lossless and lossy. Huffman Coding is a lossless-based technique used in the proposed method to maintain image quality during compression. The proposed method consists of several steps, which are dividing the image into blocks, finding the lowest value in each block and subtracting it from the rest of the values in the same block, then subtracting one from the odd numbers, dividing all the values on two, and finally applying the Huffman Coding technique to the block. The proposed method is applied to a well-known gray and color set with different types and different dimensions. Standard evaluation measures are used (i.e., PSNR, MSE, and CR) to evaluate the proposed method’s performance. When compressing images using the proposed method, the results demonstrated 0.11% enhancement when used two by two blocks. It also got high compression rates (25%). Near-lossless technique (dpeaa)DE-He213 Huffman coding (dpeaa)DE-He213 Image quality (dpeaa)DE-He213 Image compression (dpeaa)DE-He213 Abualigah, Laith aut Qawaqzeh, Mohammed K. aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 20 vom: 30. März, Seite 28509-28529 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:20 day:30 month:03 pages:28509-28529 https://dx.doi.org/10.1007/s11042-022-12846-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81 2022 20 30 03 28509-28529 |
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10.1007/s11042-022-12846-8 doi (DE-627)SPR047666498 (SPR)s11042-022-12846-8-e DE-627 ger DE-627 rakwb eng Otair, Mohammed verfasserin aut Improved near-lossless technique using the Huffman coding for enhancing the quality of image compression 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Digital data compression aims to reduce the size of digital files in line with technological development. However, most data is distinguished by its large size, which requires a large storage capacity, and requires a long time in transmission operations via the Internet. Therefore, a new compress files method is needed to reduce the image size, maintain its quality, utilize storage spaces, and minimize time. This paper aims to improve digital image compression’s compression rates by dividing the image into several blocks. Thus, a new near-lossless method using the Huffman Coding technique is proposed. Digital image compression techniques are classified as lossless and lossy. Huffman Coding is a lossless-based technique used in the proposed method to maintain image quality during compression. The proposed method consists of several steps, which are dividing the image into blocks, finding the lowest value in each block and subtracting it from the rest of the values in the same block, then subtracting one from the odd numbers, dividing all the values on two, and finally applying the Huffman Coding technique to the block. The proposed method is applied to a well-known gray and color set with different types and different dimensions. Standard evaluation measures are used (i.e., PSNR, MSE, and CR) to evaluate the proposed method’s performance. When compressing images using the proposed method, the results demonstrated 0.11% enhancement when used two by two blocks. It also got high compression rates (25%). Near-lossless technique (dpeaa)DE-He213 Huffman coding (dpeaa)DE-He213 Image quality (dpeaa)DE-He213 Image compression (dpeaa)DE-He213 Abualigah, Laith aut Qawaqzeh, Mohammed K. aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 20 vom: 30. März, Seite 28509-28529 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:20 day:30 month:03 pages:28509-28529 https://dx.doi.org/10.1007/s11042-022-12846-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81 2022 20 30 03 28509-28529 |
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10.1007/s11042-022-12846-8 doi (DE-627)SPR047666498 (SPR)s11042-022-12846-8-e DE-627 ger DE-627 rakwb eng Otair, Mohammed verfasserin aut Improved near-lossless technique using the Huffman coding for enhancing the quality of image compression 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Digital data compression aims to reduce the size of digital files in line with technological development. However, most data is distinguished by its large size, which requires a large storage capacity, and requires a long time in transmission operations via the Internet. Therefore, a new compress files method is needed to reduce the image size, maintain its quality, utilize storage spaces, and minimize time. This paper aims to improve digital image compression’s compression rates by dividing the image into several blocks. Thus, a new near-lossless method using the Huffman Coding technique is proposed. Digital image compression techniques are classified as lossless and lossy. Huffman Coding is a lossless-based technique used in the proposed method to maintain image quality during compression. The proposed method consists of several steps, which are dividing the image into blocks, finding the lowest value in each block and subtracting it from the rest of the values in the same block, then subtracting one from the odd numbers, dividing all the values on two, and finally applying the Huffman Coding technique to the block. The proposed method is applied to a well-known gray and color set with different types and different dimensions. Standard evaluation measures are used (i.e., PSNR, MSE, and CR) to evaluate the proposed method’s performance. When compressing images using the proposed method, the results demonstrated 0.11% enhancement when used two by two blocks. It also got high compression rates (25%). Near-lossless technique (dpeaa)DE-He213 Huffman coding (dpeaa)DE-He213 Image quality (dpeaa)DE-He213 Image compression (dpeaa)DE-He213 Abualigah, Laith aut Qawaqzeh, Mohammed K. aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 20 vom: 30. März, Seite 28509-28529 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:20 day:30 month:03 pages:28509-28529 https://dx.doi.org/10.1007/s11042-022-12846-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81 2022 20 30 03 28509-28529 |
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10.1007/s11042-022-12846-8 doi (DE-627)SPR047666498 (SPR)s11042-022-12846-8-e DE-627 ger DE-627 rakwb eng Otair, Mohammed verfasserin aut Improved near-lossless technique using the Huffman coding for enhancing the quality of image compression 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Digital data compression aims to reduce the size of digital files in line with technological development. However, most data is distinguished by its large size, which requires a large storage capacity, and requires a long time in transmission operations via the Internet. Therefore, a new compress files method is needed to reduce the image size, maintain its quality, utilize storage spaces, and minimize time. This paper aims to improve digital image compression’s compression rates by dividing the image into several blocks. Thus, a new near-lossless method using the Huffman Coding technique is proposed. Digital image compression techniques are classified as lossless and lossy. Huffman Coding is a lossless-based technique used in the proposed method to maintain image quality during compression. The proposed method consists of several steps, which are dividing the image into blocks, finding the lowest value in each block and subtracting it from the rest of the values in the same block, then subtracting one from the odd numbers, dividing all the values on two, and finally applying the Huffman Coding technique to the block. The proposed method is applied to a well-known gray and color set with different types and different dimensions. Standard evaluation measures are used (i.e., PSNR, MSE, and CR) to evaluate the proposed method’s performance. When compressing images using the proposed method, the results demonstrated 0.11% enhancement when used two by two blocks. It also got high compression rates (25%). Near-lossless technique (dpeaa)DE-He213 Huffman coding (dpeaa)DE-He213 Image quality (dpeaa)DE-He213 Image compression (dpeaa)DE-He213 Abualigah, Laith aut Qawaqzeh, Mohammed K. aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 20 vom: 30. März, Seite 28509-28529 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:20 day:30 month:03 pages:28509-28529 https://dx.doi.org/10.1007/s11042-022-12846-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81 2022 20 30 03 28509-28529 |
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improved near-lossless technique using the huffman coding for enhancing the quality of image compression |
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Improved near-lossless technique using the Huffman coding for enhancing the quality of image compression |
| abstract |
Abstract Digital data compression aims to reduce the size of digital files in line with technological development. However, most data is distinguished by its large size, which requires a large storage capacity, and requires a long time in transmission operations via the Internet. Therefore, a new compress files method is needed to reduce the image size, maintain its quality, utilize storage spaces, and minimize time. This paper aims to improve digital image compression’s compression rates by dividing the image into several blocks. Thus, a new near-lossless method using the Huffman Coding technique is proposed. Digital image compression techniques are classified as lossless and lossy. Huffman Coding is a lossless-based technique used in the proposed method to maintain image quality during compression. The proposed method consists of several steps, which are dividing the image into blocks, finding the lowest value in each block and subtracting it from the rest of the values in the same block, then subtracting one from the odd numbers, dividing all the values on two, and finally applying the Huffman Coding technique to the block. The proposed method is applied to a well-known gray and color set with different types and different dimensions. Standard evaluation measures are used (i.e., PSNR, MSE, and CR) to evaluate the proposed method’s performance. When compressing images using the proposed method, the results demonstrated 0.11% enhancement when used two by two blocks. It also got high compression rates (25%). © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstractGer |
Abstract Digital data compression aims to reduce the size of digital files in line with technological development. However, most data is distinguished by its large size, which requires a large storage capacity, and requires a long time in transmission operations via the Internet. Therefore, a new compress files method is needed to reduce the image size, maintain its quality, utilize storage spaces, and minimize time. This paper aims to improve digital image compression’s compression rates by dividing the image into several blocks. Thus, a new near-lossless method using the Huffman Coding technique is proposed. Digital image compression techniques are classified as lossless and lossy. Huffman Coding is a lossless-based technique used in the proposed method to maintain image quality during compression. The proposed method consists of several steps, which are dividing the image into blocks, finding the lowest value in each block and subtracting it from the rest of the values in the same block, then subtracting one from the odd numbers, dividing all the values on two, and finally applying the Huffman Coding technique to the block. The proposed method is applied to a well-known gray and color set with different types and different dimensions. Standard evaluation measures are used (i.e., PSNR, MSE, and CR) to evaluate the proposed method’s performance. When compressing images using the proposed method, the results demonstrated 0.11% enhancement when used two by two blocks. It also got high compression rates (25%). © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract Digital data compression aims to reduce the size of digital files in line with technological development. However, most data is distinguished by its large size, which requires a large storage capacity, and requires a long time in transmission operations via the Internet. Therefore, a new compress files method is needed to reduce the image size, maintain its quality, utilize storage spaces, and minimize time. This paper aims to improve digital image compression’s compression rates by dividing the image into several blocks. Thus, a new near-lossless method using the Huffman Coding technique is proposed. Digital image compression techniques are classified as lossless and lossy. Huffman Coding is a lossless-based technique used in the proposed method to maintain image quality during compression. The proposed method consists of several steps, which are dividing the image into blocks, finding the lowest value in each block and subtracting it from the rest of the values in the same block, then subtracting one from the odd numbers, dividing all the values on two, and finally applying the Huffman Coding technique to the block. The proposed method is applied to a well-known gray and color set with different types and different dimensions. Standard evaluation measures are used (i.e., PSNR, MSE, and CR) to evaluate the proposed method’s performance. When compressing images using the proposed method, the results demonstrated 0.11% enhancement when used two by two blocks. It also got high compression rates (25%). © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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Improved near-lossless technique using the Huffman coding for enhancing the quality of image compression |
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Abualigah, Laith Qawaqzeh, Mohammed K. |
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10.1007/s11042-022-12846-8 |
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| score |
7.3990602 |