An adaptive fractal-based image coding with hierarchical classification strategy and its modifications

Abstract Fractal-based image coding is one of the efficient methods for grayscale image since the reconstructed images are resolution independent and also has low reconstruction time. This paper discusses the efficiency of hierarchical classification strategy for fractal image coding that uses adapt...
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Autor*in:	Nandi, Utpal [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Fractal image coding Hierarchical classification PSNR Loss-less coding Adaptive quadtree partitioning

Übergeordnetes Werk:	Enthalten in: Innovations in systems and software engineering - London : Springer, 2005, 15(2019), 1 vom: 12. Feb., Seite 35-42
Übergeordnetes Werk:	volume:15 ; year:2019 ; number:1 ; day:12 ; month:02 ; pages:35-42

Links:	Volltext

DOI / URN:	10.1007/s11334-019-00327-5

Katalog-ID:	SPR018756611

Internformat


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245	1	3	\|a An adaptive fractal-based image coding with hierarchical classification strategy and its modifications
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520			\|a Abstract Fractal-based image coding is one of the efficient methods for grayscale image since the reconstructed images are resolution independent and also has low reconstruction time. This paper discusses the efficiency of hierarchical classification strategy for fractal image coding that uses adaptive quadtree partitioning. The scheme forms two level hierarchical domain groups and ranges are matched with similar hierarchical domain class. The fractal image coding technique with hierarchical classification strategy is then modified also to improve the compression ratio by using an efficient loss-less coding scheme OLZW on fractal compressed image. A variant of OLZW, i.e., MOLZW is also applied for the same. These modified variants show their significant improvements in compression ratio without degradation of image quality.
650		4	\|a Fractal image coding \|7 (dpeaa)DE-He213
650		4	\|a Hierarchical classification \|7 (dpeaa)DE-He213
650		4	\|a PSNR \|7 (dpeaa)DE-He213
650		4	\|a Loss-less coding \|7 (dpeaa)DE-He213
650		4	\|a Adaptive quadtree partitioning \|7 (dpeaa)DE-He213
773	0	8	\|i Enthalten in \|t Innovations in systems and software engineering \|d London : Springer, 2005 \|g 15(2019), 1 vom: 12. Feb., Seite 35-42 \|w (DE-627)493205713 \|w (DE-600)2195084-2 \|x 1614-5054 \|7 nnns
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912			\|a GBV_ILN_32
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_120
912			\|a GBV_ILN_138
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_152
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_250
912			\|a GBV_ILN_281
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_636
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2031
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2037
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2039
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2057
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2070
912			\|a GBV_ILN_2086
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2093
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2107
912			\|a GBV_ILN_2108
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2116
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2119
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2144
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2188
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2446
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2472
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_2548
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4046
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4246
912			\|a GBV_ILN_4249
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912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
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912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
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912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4336
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936	b	k	\|a 54.50 \|q ASE
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952			\|d 15 \|j 2019 \|e 1 \|b 12 \|c 02 \|h 35-42

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author_variant	u n un
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publishDate	2019
allfields	10.1007/s11334-019-00327-5 doi (DE-627)SPR018756611 (SPR)s11334-019-00327-5-e DE-627 ger DE-627 rakwb eng 004 ASE 54.50 bkl Nandi, Utpal verfasserin aut An adaptive fractal-based image coding with hierarchical classification strategy and its modifications 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Fractal-based image coding is one of the efficient methods for grayscale image since the reconstructed images are resolution independent and also has low reconstruction time. This paper discusses the efficiency of hierarchical classification strategy for fractal image coding that uses adaptive quadtree partitioning. The scheme forms two level hierarchical domain groups and ranges are matched with similar hierarchical domain class. The fractal image coding technique with hierarchical classification strategy is then modified also to improve the compression ratio by using an efficient loss-less coding scheme OLZW on fractal compressed image. A variant of OLZW, i.e., MOLZW is also applied for the same. These modified variants show their significant improvements in compression ratio without degradation of image quality. Fractal image coding (dpeaa)DE-He213 Hierarchical classification (dpeaa)DE-He213 PSNR (dpeaa)DE-He213 Loss-less coding (dpeaa)DE-He213 Adaptive quadtree partitioning (dpeaa)DE-He213 Enthalten in Innovations in systems and software engineering London : Springer, 2005 15(2019), 1 vom: 12. Feb., Seite 35-42 (DE-627)493205713 (DE-600)2195084-2 1614-5054 nnns volume:15 year:2019 number:1 day:12 month:02 pages:35-42 https://dx.doi.org/10.1007/s11334-019-00327-5 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 54.50 ASE AR 15 2019 1 12 02 35-42
spelling	10.1007/s11334-019-00327-5 doi (DE-627)SPR018756611 (SPR)s11334-019-00327-5-e DE-627 ger DE-627 rakwb eng 004 ASE 54.50 bkl Nandi, Utpal verfasserin aut An adaptive fractal-based image coding with hierarchical classification strategy and its modifications 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Fractal-based image coding is one of the efficient methods for grayscale image since the reconstructed images are resolution independent and also has low reconstruction time. This paper discusses the efficiency of hierarchical classification strategy for fractal image coding that uses adaptive quadtree partitioning. The scheme forms two level hierarchical domain groups and ranges are matched with similar hierarchical domain class. The fractal image coding technique with hierarchical classification strategy is then modified also to improve the compression ratio by using an efficient loss-less coding scheme OLZW on fractal compressed image. A variant of OLZW, i.e., MOLZW is also applied for the same. These modified variants show their significant improvements in compression ratio without degradation of image quality. Fractal image coding (dpeaa)DE-He213 Hierarchical classification (dpeaa)DE-He213 PSNR (dpeaa)DE-He213 Loss-less coding (dpeaa)DE-He213 Adaptive quadtree partitioning (dpeaa)DE-He213 Enthalten in Innovations in systems and software engineering London : Springer, 2005 15(2019), 1 vom: 12. Feb., Seite 35-42 (DE-627)493205713 (DE-600)2195084-2 1614-5054 nnns volume:15 year:2019 number:1 day:12 month:02 pages:35-42 https://dx.doi.org/10.1007/s11334-019-00327-5 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 54.50 ASE AR 15 2019 1 12 02 35-42
allfields_unstemmed	10.1007/s11334-019-00327-5 doi (DE-627)SPR018756611 (SPR)s11334-019-00327-5-e DE-627 ger DE-627 rakwb eng 004 ASE 54.50 bkl Nandi, Utpal verfasserin aut An adaptive fractal-based image coding with hierarchical classification strategy and its modifications 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Fractal-based image coding is one of the efficient methods for grayscale image since the reconstructed images are resolution independent and also has low reconstruction time. This paper discusses the efficiency of hierarchical classification strategy for fractal image coding that uses adaptive quadtree partitioning. The scheme forms two level hierarchical domain groups and ranges are matched with similar hierarchical domain class. The fractal image coding technique with hierarchical classification strategy is then modified also to improve the compression ratio by using an efficient loss-less coding scheme OLZW on fractal compressed image. A variant of OLZW, i.e., MOLZW is also applied for the same. These modified variants show their significant improvements in compression ratio without degradation of image quality. Fractal image coding (dpeaa)DE-He213 Hierarchical classification (dpeaa)DE-He213 PSNR (dpeaa)DE-He213 Loss-less coding (dpeaa)DE-He213 Adaptive quadtree partitioning (dpeaa)DE-He213 Enthalten in Innovations in systems and software engineering London : Springer, 2005 15(2019), 1 vom: 12. Feb., Seite 35-42 (DE-627)493205713 (DE-600)2195084-2 1614-5054 nnns volume:15 year:2019 number:1 day:12 month:02 pages:35-42 https://dx.doi.org/10.1007/s11334-019-00327-5 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 54.50 ASE AR 15 2019 1 12 02 35-42
allfieldsGer	10.1007/s11334-019-00327-5 doi (DE-627)SPR018756611 (SPR)s11334-019-00327-5-e DE-627 ger DE-627 rakwb eng 004 ASE 54.50 bkl Nandi, Utpal verfasserin aut An adaptive fractal-based image coding with hierarchical classification strategy and its modifications 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Fractal-based image coding is one of the efficient methods for grayscale image since the reconstructed images are resolution independent and also has low reconstruction time. This paper discusses the efficiency of hierarchical classification strategy for fractal image coding that uses adaptive quadtree partitioning. The scheme forms two level hierarchical domain groups and ranges are matched with similar hierarchical domain class. The fractal image coding technique with hierarchical classification strategy is then modified also to improve the compression ratio by using an efficient loss-less coding scheme OLZW on fractal compressed image. A variant of OLZW, i.e., MOLZW is also applied for the same. These modified variants show their significant improvements in compression ratio without degradation of image quality. Fractal image coding (dpeaa)DE-He213 Hierarchical classification (dpeaa)DE-He213 PSNR (dpeaa)DE-He213 Loss-less coding (dpeaa)DE-He213 Adaptive quadtree partitioning (dpeaa)DE-He213 Enthalten in Innovations in systems and software engineering London : Springer, 2005 15(2019), 1 vom: 12. Feb., Seite 35-42 (DE-627)493205713 (DE-600)2195084-2 1614-5054 nnns volume:15 year:2019 number:1 day:12 month:02 pages:35-42 https://dx.doi.org/10.1007/s11334-019-00327-5 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 54.50 ASE AR 15 2019 1 12 02 35-42
allfieldsSound	10.1007/s11334-019-00327-5 doi (DE-627)SPR018756611 (SPR)s11334-019-00327-5-e DE-627 ger DE-627 rakwb eng 004 ASE 54.50 bkl Nandi, Utpal verfasserin aut An adaptive fractal-based image coding with hierarchical classification strategy and its modifications 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Fractal-based image coding is one of the efficient methods for grayscale image since the reconstructed images are resolution independent and also has low reconstruction time. This paper discusses the efficiency of hierarchical classification strategy for fractal image coding that uses adaptive quadtree partitioning. The scheme forms two level hierarchical domain groups and ranges are matched with similar hierarchical domain class. The fractal image coding technique with hierarchical classification strategy is then modified also to improve the compression ratio by using an efficient loss-less coding scheme OLZW on fractal compressed image. A variant of OLZW, i.e., MOLZW is also applied for the same. These modified variants show their significant improvements in compression ratio without degradation of image quality. Fractal image coding (dpeaa)DE-He213 Hierarchical classification (dpeaa)DE-He213 PSNR (dpeaa)DE-He213 Loss-less coding (dpeaa)DE-He213 Adaptive quadtree partitioning (dpeaa)DE-He213 Enthalten in Innovations in systems and software engineering London : Springer, 2005 15(2019), 1 vom: 12. Feb., Seite 35-42 (DE-627)493205713 (DE-600)2195084-2 1614-5054 nnns volume:15 year:2019 number:1 day:12 month:02 pages:35-42 https://dx.doi.org/10.1007/s11334-019-00327-5 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 54.50 ASE AR 15 2019 1 12 02 35-42
language	English
source	Enthalten in Innovations in systems and software engineering 15(2019), 1 vom: 12. Feb., Seite 35-42 volume:15 year:2019 number:1 day:12 month:02 pages:35-42
sourceStr	Enthalten in Innovations in systems and software engineering 15(2019), 1 vom: 12. Feb., Seite 35-42 volume:15 year:2019 number:1 day:12 month:02 pages:35-42
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Fractal image coding Hierarchical classification PSNR Loss-less coding Adaptive quadtree partitioning
dewey-raw	004
isfreeaccess_bool	false
container_title	Innovations in systems and software engineering
authorswithroles_txt_mv	Nandi, Utpal @@aut@@
publishDateDaySort_date	2019-02-12T00:00:00Z
hierarchy_top_id	493205713
dewey-sort	14
id	SPR018756611
language_de	englisch
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author	Nandi, Utpal
spellingShingle	Nandi, Utpal ddc 004 bkl 54.50 misc Fractal image coding misc Hierarchical classification misc PSNR misc Loss-less coding misc Adaptive quadtree partitioning An adaptive fractal-based image coding with hierarchical classification strategy and its modifications
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topic_title	004 ASE 54.50 bkl An adaptive fractal-based image coding with hierarchical classification strategy and its modifications Fractal image coding (dpeaa)DE-He213 Hierarchical classification (dpeaa)DE-He213 PSNR (dpeaa)DE-He213 Loss-less coding (dpeaa)DE-He213 Adaptive quadtree partitioning (dpeaa)DE-He213
topic	ddc 004 bkl 54.50 misc Fractal image coding misc Hierarchical classification misc PSNR misc Loss-less coding misc Adaptive quadtree partitioning
topic_unstemmed	ddc 004 bkl 54.50 misc Fractal image coding misc Hierarchical classification misc PSNR misc Loss-less coding misc Adaptive quadtree partitioning
topic_browse	ddc 004 bkl 54.50 misc Fractal image coding misc Hierarchical classification misc PSNR misc Loss-less coding misc Adaptive quadtree partitioning
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title	An adaptive fractal-based image coding with hierarchical classification strategy and its modifications
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title_full	An adaptive fractal-based image coding with hierarchical classification strategy and its modifications
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title_sort	adaptive fractal-based image coding with hierarchical classification strategy and its modifications
title_auth	An adaptive fractal-based image coding with hierarchical classification strategy and its modifications
abstract	Abstract Fractal-based image coding is one of the efficient methods for grayscale image since the reconstructed images are resolution independent and also has low reconstruction time. This paper discusses the efficiency of hierarchical classification strategy for fractal image coding that uses adaptive quadtree partitioning. The scheme forms two level hierarchical domain groups and ranges are matched with similar hierarchical domain class. The fractal image coding technique with hierarchical classification strategy is then modified also to improve the compression ratio by using an efficient loss-less coding scheme OLZW on fractal compressed image. A variant of OLZW, i.e., MOLZW is also applied for the same. These modified variants show their significant improvements in compression ratio without degradation of image quality.
abstractGer	Abstract Fractal-based image coding is one of the efficient methods for grayscale image since the reconstructed images are resolution independent and also has low reconstruction time. This paper discusses the efficiency of hierarchical classification strategy for fractal image coding that uses adaptive quadtree partitioning. The scheme forms two level hierarchical domain groups and ranges are matched with similar hierarchical domain class. The fractal image coding technique with hierarchical classification strategy is then modified also to improve the compression ratio by using an efficient loss-less coding scheme OLZW on fractal compressed image. A variant of OLZW, i.e., MOLZW is also applied for the same. These modified variants show their significant improvements in compression ratio without degradation of image quality.
abstract_unstemmed	Abstract Fractal-based image coding is one of the efficient methods for grayscale image since the reconstructed images are resolution independent and also has low reconstruction time. This paper discusses the efficiency of hierarchical classification strategy for fractal image coding that uses adaptive quadtree partitioning. The scheme forms two level hierarchical domain groups and ranges are matched with similar hierarchical domain class. The fractal image coding technique with hierarchical classification strategy is then modified also to improve the compression ratio by using an efficient loss-less coding scheme OLZW on fractal compressed image. A variant of OLZW, i.e., MOLZW is also applied for the same. These modified variants show their significant improvements in compression ratio without degradation of image quality.
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title_short	An adaptive fractal-based image coding with hierarchical classification strategy and its modifications
url	https://dx.doi.org/10.1007/s11334-019-00327-5
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up_date	2024-07-03T22:00:52.836Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR018756611</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111062604.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201006s2019 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11334-019-00327-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR018756611</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11334-019-00327-5-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.50</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Nandi, Utpal</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="3"><subfield code="a">An adaptive fractal-based image coding with hierarchical classification strategy and its modifications</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Fractal-based image coding is one of the efficient methods for grayscale image since the reconstructed images are resolution independent and also has low reconstruction time. This paper discusses the efficiency of hierarchical classification strategy for fractal image coding that uses adaptive quadtree partitioning. The scheme forms two level hierarchical domain groups and ranges are matched with similar hierarchical domain class. The fractal image coding technique with hierarchical classification strategy is then modified also to improve the compression ratio by using an efficient loss-less coding scheme OLZW on fractal compressed image. A variant of OLZW, i.e., MOLZW is also applied for the same. 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An adaptive fractal-based image coding with hierarchical classification strategy and its modifications

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