Lossless image compression using gradient based space filling curves (G-SFC)
Abstract In most classical lossless image compression schemes, images are scanned line by line, and so, only horizontal patterns are effectively compressed. The proposed approach attempts to better explore image correlation in different directions by adopting a context-based adaptive scanning proces...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ouni, Tarek [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2013 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag London 2013 |
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| Übergeordnetes Werk: |
Enthalten in: Signal, image and video processing - London [u.a.] : Springer, 2007, 9(2013), 2 vom: 10. März, Seite 277-293 |
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| Übergeordnetes Werk: |
volume:9 ; year:2013 ; number:2 ; day:10 ; month:03 ; pages:277-293 |
| Links: |
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| DOI / URN: |
10.1007/s11760-013-0435-4 |
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| Katalog-ID: |
SPR022269509 |
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| 520 | |a Abstract In most classical lossless image compression schemes, images are scanned line by line, and so, only horizontal patterns are effectively compressed. The proposed approach attempts to better explore image correlation in different directions by adopting a context-based adaptive scanning process. The adopted scanning process aims to generate a compact one-dimensional image representation by using an image gradient based scan process. This process tries to find the best space-filling curve that ensures scanning the image according to the direction where minimal pixels’ intensity change is found. Such scan process would reduce high frequency data. It is used in order to provide an easily compressible smooth and highly correlated mono-dimensional signal. The suggested representation acts as a pre-processing which transforms the image source into some strongly correlated representation before applying coding algorithms. Based on this representation, a new lossless image compression method is designed. Our experimental results show that the proposed image representation is able to significantly improve the signal proprieties in terms of correlation and monotony and then compression performances. The suggested coding scheme shows a competitive compression results compared to conventional lossless coding schemes such as PNG and JPEG 2000. | ||
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| 650 | 4 | |a Scan |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Gradient |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Lassoued, Arij |4 aut | |
| 700 | 1 | |a Abid, Mohamed |4 aut | |
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10.1007/s11760-013-0435-4 doi (DE-627)SPR022269509 (SPR)s11760-013-0435-4-e DE-627 ger DE-627 rakwb eng Ouni, Tarek verfasserin aut Lossless image compression using gradient based space filling curves (G-SFC) 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2013 Abstract In most classical lossless image compression schemes, images are scanned line by line, and so, only horizontal patterns are effectively compressed. The proposed approach attempts to better explore image correlation in different directions by adopting a context-based adaptive scanning process. The adopted scanning process aims to generate a compact one-dimensional image representation by using an image gradient based scan process. This process tries to find the best space-filling curve that ensures scanning the image according to the direction where minimal pixels’ intensity change is found. Such scan process would reduce high frequency data. It is used in order to provide an easily compressible smooth and highly correlated mono-dimensional signal. The suggested representation acts as a pre-processing which transforms the image source into some strongly correlated representation before applying coding algorithms. Based on this representation, a new lossless image compression method is designed. Our experimental results show that the proposed image representation is able to significantly improve the signal proprieties in terms of correlation and monotony and then compression performances. The suggested coding scheme shows a competitive compression results compared to conventional lossless coding schemes such as PNG and JPEG 2000. Image (dpeaa)DE-He213 Lossless (dpeaa)DE-He213 Coding (dpeaa)DE-He213 Scan (dpeaa)DE-He213 Gradient (dpeaa)DE-He213 SFC (dpeaa)DE-He213 Lassoued, Arij aut Abid, Mohamed aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 9(2013), 2 vom: 10. März, Seite 277-293 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:9 year:2013 number:2 day:10 month:03 pages:277-293 https://dx.doi.org/10.1007/s11760-013-0435-4 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_65 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 9 2013 2 10 03 277-293 |
| spelling |
10.1007/s11760-013-0435-4 doi (DE-627)SPR022269509 (SPR)s11760-013-0435-4-e DE-627 ger DE-627 rakwb eng Ouni, Tarek verfasserin aut Lossless image compression using gradient based space filling curves (G-SFC) 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2013 Abstract In most classical lossless image compression schemes, images are scanned line by line, and so, only horizontal patterns are effectively compressed. The proposed approach attempts to better explore image correlation in different directions by adopting a context-based adaptive scanning process. The adopted scanning process aims to generate a compact one-dimensional image representation by using an image gradient based scan process. This process tries to find the best space-filling curve that ensures scanning the image according to the direction where minimal pixels’ intensity change is found. Such scan process would reduce high frequency data. It is used in order to provide an easily compressible smooth and highly correlated mono-dimensional signal. The suggested representation acts as a pre-processing which transforms the image source into some strongly correlated representation before applying coding algorithms. Based on this representation, a new lossless image compression method is designed. Our experimental results show that the proposed image representation is able to significantly improve the signal proprieties in terms of correlation and monotony and then compression performances. The suggested coding scheme shows a competitive compression results compared to conventional lossless coding schemes such as PNG and JPEG 2000. Image (dpeaa)DE-He213 Lossless (dpeaa)DE-He213 Coding (dpeaa)DE-He213 Scan (dpeaa)DE-He213 Gradient (dpeaa)DE-He213 SFC (dpeaa)DE-He213 Lassoued, Arij aut Abid, Mohamed aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 9(2013), 2 vom: 10. März, Seite 277-293 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:9 year:2013 number:2 day:10 month:03 pages:277-293 https://dx.doi.org/10.1007/s11760-013-0435-4 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_65 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 9 2013 2 10 03 277-293 |
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10.1007/s11760-013-0435-4 doi (DE-627)SPR022269509 (SPR)s11760-013-0435-4-e DE-627 ger DE-627 rakwb eng Ouni, Tarek verfasserin aut Lossless image compression using gradient based space filling curves (G-SFC) 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2013 Abstract In most classical lossless image compression schemes, images are scanned line by line, and so, only horizontal patterns are effectively compressed. The proposed approach attempts to better explore image correlation in different directions by adopting a context-based adaptive scanning process. The adopted scanning process aims to generate a compact one-dimensional image representation by using an image gradient based scan process. This process tries to find the best space-filling curve that ensures scanning the image according to the direction where minimal pixels’ intensity change is found. Such scan process would reduce high frequency data. It is used in order to provide an easily compressible smooth and highly correlated mono-dimensional signal. The suggested representation acts as a pre-processing which transforms the image source into some strongly correlated representation before applying coding algorithms. Based on this representation, a new lossless image compression method is designed. Our experimental results show that the proposed image representation is able to significantly improve the signal proprieties in terms of correlation and monotony and then compression performances. The suggested coding scheme shows a competitive compression results compared to conventional lossless coding schemes such as PNG and JPEG 2000. Image (dpeaa)DE-He213 Lossless (dpeaa)DE-He213 Coding (dpeaa)DE-He213 Scan (dpeaa)DE-He213 Gradient (dpeaa)DE-He213 SFC (dpeaa)DE-He213 Lassoued, Arij aut Abid, Mohamed aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 9(2013), 2 vom: 10. März, Seite 277-293 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:9 year:2013 number:2 day:10 month:03 pages:277-293 https://dx.doi.org/10.1007/s11760-013-0435-4 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_65 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 9 2013 2 10 03 277-293 |
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10.1007/s11760-013-0435-4 doi (DE-627)SPR022269509 (SPR)s11760-013-0435-4-e DE-627 ger DE-627 rakwb eng Ouni, Tarek verfasserin aut Lossless image compression using gradient based space filling curves (G-SFC) 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2013 Abstract In most classical lossless image compression schemes, images are scanned line by line, and so, only horizontal patterns are effectively compressed. The proposed approach attempts to better explore image correlation in different directions by adopting a context-based adaptive scanning process. The adopted scanning process aims to generate a compact one-dimensional image representation by using an image gradient based scan process. This process tries to find the best space-filling curve that ensures scanning the image according to the direction where minimal pixels’ intensity change is found. Such scan process would reduce high frequency data. It is used in order to provide an easily compressible smooth and highly correlated mono-dimensional signal. The suggested representation acts as a pre-processing which transforms the image source into some strongly correlated representation before applying coding algorithms. Based on this representation, a new lossless image compression method is designed. Our experimental results show that the proposed image representation is able to significantly improve the signal proprieties in terms of correlation and monotony and then compression performances. The suggested coding scheme shows a competitive compression results compared to conventional lossless coding schemes such as PNG and JPEG 2000. Image (dpeaa)DE-He213 Lossless (dpeaa)DE-He213 Coding (dpeaa)DE-He213 Scan (dpeaa)DE-He213 Gradient (dpeaa)DE-He213 SFC (dpeaa)DE-He213 Lassoued, Arij aut Abid, Mohamed aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 9(2013), 2 vom: 10. März, Seite 277-293 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:9 year:2013 number:2 day:10 month:03 pages:277-293 https://dx.doi.org/10.1007/s11760-013-0435-4 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_65 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 9 2013 2 10 03 277-293 |
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10.1007/s11760-013-0435-4 doi (DE-627)SPR022269509 (SPR)s11760-013-0435-4-e DE-627 ger DE-627 rakwb eng Ouni, Tarek verfasserin aut Lossless image compression using gradient based space filling curves (G-SFC) 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2013 Abstract In most classical lossless image compression schemes, images are scanned line by line, and so, only horizontal patterns are effectively compressed. The proposed approach attempts to better explore image correlation in different directions by adopting a context-based adaptive scanning process. The adopted scanning process aims to generate a compact one-dimensional image representation by using an image gradient based scan process. This process tries to find the best space-filling curve that ensures scanning the image according to the direction where minimal pixels’ intensity change is found. Such scan process would reduce high frequency data. It is used in order to provide an easily compressible smooth and highly correlated mono-dimensional signal. The suggested representation acts as a pre-processing which transforms the image source into some strongly correlated representation before applying coding algorithms. Based on this representation, a new lossless image compression method is designed. Our experimental results show that the proposed image representation is able to significantly improve the signal proprieties in terms of correlation and monotony and then compression performances. The suggested coding scheme shows a competitive compression results compared to conventional lossless coding schemes such as PNG and JPEG 2000. Image (dpeaa)DE-He213 Lossless (dpeaa)DE-He213 Coding (dpeaa)DE-He213 Scan (dpeaa)DE-He213 Gradient (dpeaa)DE-He213 SFC (dpeaa)DE-He213 Lassoued, Arij aut Abid, Mohamed aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 9(2013), 2 vom: 10. März, Seite 277-293 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:9 year:2013 number:2 day:10 month:03 pages:277-293 https://dx.doi.org/10.1007/s11760-013-0435-4 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_65 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 9 2013 2 10 03 277-293 |
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Ouni, Tarek |
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Lossless image compression using gradient based space filling curves (G-SFC) Image (dpeaa)DE-He213 Lossless (dpeaa)DE-He213 Coding (dpeaa)DE-He213 Scan (dpeaa)DE-He213 Gradient (dpeaa)DE-He213 SFC (dpeaa)DE-He213 |
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lossless image compression using gradient based space filling curves (g-sfc) |
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Lossless image compression using gradient based space filling curves (G-SFC) |
| abstract |
Abstract In most classical lossless image compression schemes, images are scanned line by line, and so, only horizontal patterns are effectively compressed. The proposed approach attempts to better explore image correlation in different directions by adopting a context-based adaptive scanning process. The adopted scanning process aims to generate a compact one-dimensional image representation by using an image gradient based scan process. This process tries to find the best space-filling curve that ensures scanning the image according to the direction where minimal pixels’ intensity change is found. Such scan process would reduce high frequency data. It is used in order to provide an easily compressible smooth and highly correlated mono-dimensional signal. The suggested representation acts as a pre-processing which transforms the image source into some strongly correlated representation before applying coding algorithms. Based on this representation, a new lossless image compression method is designed. Our experimental results show that the proposed image representation is able to significantly improve the signal proprieties in terms of correlation and monotony and then compression performances. The suggested coding scheme shows a competitive compression results compared to conventional lossless coding schemes such as PNG and JPEG 2000. © Springer-Verlag London 2013 |
| abstractGer |
Abstract In most classical lossless image compression schemes, images are scanned line by line, and so, only horizontal patterns are effectively compressed. The proposed approach attempts to better explore image correlation in different directions by adopting a context-based adaptive scanning process. The adopted scanning process aims to generate a compact one-dimensional image representation by using an image gradient based scan process. This process tries to find the best space-filling curve that ensures scanning the image according to the direction where minimal pixels’ intensity change is found. Such scan process would reduce high frequency data. It is used in order to provide an easily compressible smooth and highly correlated mono-dimensional signal. The suggested representation acts as a pre-processing which transforms the image source into some strongly correlated representation before applying coding algorithms. Based on this representation, a new lossless image compression method is designed. Our experimental results show that the proposed image representation is able to significantly improve the signal proprieties in terms of correlation and monotony and then compression performances. The suggested coding scheme shows a competitive compression results compared to conventional lossless coding schemes such as PNG and JPEG 2000. © Springer-Verlag London 2013 |
| abstract_unstemmed |
Abstract In most classical lossless image compression schemes, images are scanned line by line, and so, only horizontal patterns are effectively compressed. The proposed approach attempts to better explore image correlation in different directions by adopting a context-based adaptive scanning process. The adopted scanning process aims to generate a compact one-dimensional image representation by using an image gradient based scan process. This process tries to find the best space-filling curve that ensures scanning the image according to the direction where minimal pixels’ intensity change is found. Such scan process would reduce high frequency data. It is used in order to provide an easily compressible smooth and highly correlated mono-dimensional signal. The suggested representation acts as a pre-processing which transforms the image source into some strongly correlated representation before applying coding algorithms. Based on this representation, a new lossless image compression method is designed. Our experimental results show that the proposed image representation is able to significantly improve the signal proprieties in terms of correlation and monotony and then compression performances. The suggested coding scheme shows a competitive compression results compared to conventional lossless coding schemes such as PNG and JPEG 2000. © Springer-Verlag London 2013 |
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Lossless image compression using gradient based space filling curves (G-SFC) |
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https://dx.doi.org/10.1007/s11760-013-0435-4 |
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Lassoued, Arij Abid, Mohamed |
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| doi_str |
10.1007/s11760-013-0435-4 |
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2024-07-04T02:29:46.607Z |
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| score |
7.4001713 |