Fractal image compression with adaptive quadtree partitioning and non-linear affine map

Abstract Fractal image compression techniques are now very popular for its high compression rates and resolution independence property. However, the qualities of decoded images of the existing techniques are not satisfactory. An adaptive partitioning scheme can improve the image quality significantl...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Nandi, Utpal [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Fractal compression Adaptive quadtree partition Affine map Non-linear affine map Contractive transform Compression ratio Image quality

Übergeordnetes Werk:	Enthalten in: Multimedia tools and applications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 79(2020), 35-36 vom: 14. Juli, Seite 26345-26368
Übergeordnetes Werk:	volume:79 ; year:2020 ; number:35-36 ; day:14 ; month:07 ; pages:26345-26368

Links:	Volltext

DOI / URN:	10.1007/s11042-020-09256-z

Katalog-ID:	SPR04074034X

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520			\|a Abstract Fractal image compression techniques are now very popular for its high compression rates and resolution independence property. However, the qualities of decoded images of the existing techniques are not satisfactory. An adaptive partitioning scheme can improve the image quality significantly. These existing adaptive techniques use linear affine maps during encoding that have limited pixel intensity approximation ability. In order to increase the image quality further, non-linear affine maps can be used that generalizes the pixel intensity approximation and generates much better approximation. Here, a fractal based technique for image compression using non-linear contractive affine maps has been proposed that applies adaptive quadtree partitioning to partition image in a context dependent way to enhance decoded image quality. The technique partitions twice an image to be compressed to obtain collection of ranges and domains and finds the highest matching non-linear affine transformed domain of each range. The corresponding affine parameters are kept in the compressed file. However, a range may be broken into sub-ranges using adaptive quadtree partitioning for unavailability of enough matching domains and repeat the same on those. The comparative results show that the proposed technique greatly improves the decoded image quality than existing techniques and also maintains the high compression ratios. Two variants have also been proposed that improve compression ratio of the proposed technique without any degradation of image quality using loss-less coding.
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650		4	\|a Adaptive quadtree partition \|7 (dpeaa)DE-He213
650		4	\|a Affine map \|7 (dpeaa)DE-He213
650		4	\|a Non-linear affine map \|7 (dpeaa)DE-He213
650		4	\|a Contractive transform \|7 (dpeaa)DE-He213
650		4	\|a Compression ratio \|7 (dpeaa)DE-He213
650		4	\|a Image quality \|7 (dpeaa)DE-He213
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matchkey_str	article:15737721:2020----::rcaiaeopesowtaatvqatepriinna
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publishDate	2020
allfields	10.1007/s11042-020-09256-z doi (DE-627)SPR04074034X (SPR)s11042-020-09256-z-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Nandi, Utpal verfasserin aut Fractal image compression with adaptive quadtree partitioning and non-linear affine map 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Fractal image compression techniques are now very popular for its high compression rates and resolution independence property. However, the qualities of decoded images of the existing techniques are not satisfactory. An adaptive partitioning scheme can improve the image quality significantly. These existing adaptive techniques use linear affine maps during encoding that have limited pixel intensity approximation ability. In order to increase the image quality further, non-linear affine maps can be used that generalizes the pixel intensity approximation and generates much better approximation. Here, a fractal based technique for image compression using non-linear contractive affine maps has been proposed that applies adaptive quadtree partitioning to partition image in a context dependent way to enhance decoded image quality. The technique partitions twice an image to be compressed to obtain collection of ranges and domains and finds the highest matching non-linear affine transformed domain of each range. The corresponding affine parameters are kept in the compressed file. However, a range may be broken into sub-ranges using adaptive quadtree partitioning for unavailability of enough matching domains and repeat the same on those. The comparative results show that the proposed technique greatly improves the decoded image quality than existing techniques and also maintains the high compression ratios. Two variants have also been proposed that improve compression ratio of the proposed technique without any degradation of image quality using loss-less coding. Fractal compression (dpeaa)DE-He213 Adaptive quadtree partition (dpeaa)DE-He213 Affine map (dpeaa)DE-He213 Non-linear affine map (dpeaa)DE-He213 Contractive transform (dpeaa)DE-He213 Compression ratio (dpeaa)DE-He213 Image quality (dpeaa)DE-He213 Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 79(2020), 35-36 vom: 14. Juli, Seite 26345-26368 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:79 year:2020 number:35-36 day:14 month:07 pages:26345-26368 https://dx.doi.org/10.1007/s11042-020-09256-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE 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 54.87 ASE AR 79 2020 35-36 14 07 26345-26368
spelling	10.1007/s11042-020-09256-z doi (DE-627)SPR04074034X (SPR)s11042-020-09256-z-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Nandi, Utpal verfasserin aut Fractal image compression with adaptive quadtree partitioning and non-linear affine map 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Fractal image compression techniques are now very popular for its high compression rates and resolution independence property. However, the qualities of decoded images of the existing techniques are not satisfactory. An adaptive partitioning scheme can improve the image quality significantly. These existing adaptive techniques use linear affine maps during encoding that have limited pixel intensity approximation ability. In order to increase the image quality further, non-linear affine maps can be used that generalizes the pixel intensity approximation and generates much better approximation. Here, a fractal based technique for image compression using non-linear contractive affine maps has been proposed that applies adaptive quadtree partitioning to partition image in a context dependent way to enhance decoded image quality. The technique partitions twice an image to be compressed to obtain collection of ranges and domains and finds the highest matching non-linear affine transformed domain of each range. The corresponding affine parameters are kept in the compressed file. However, a range may be broken into sub-ranges using adaptive quadtree partitioning for unavailability of enough matching domains and repeat the same on those. The comparative results show that the proposed technique greatly improves the decoded image quality than existing techniques and also maintains the high compression ratios. Two variants have also been proposed that improve compression ratio of the proposed technique without any degradation of image quality using loss-less coding. Fractal compression (dpeaa)DE-He213 Adaptive quadtree partition (dpeaa)DE-He213 Affine map (dpeaa)DE-He213 Non-linear affine map (dpeaa)DE-He213 Contractive transform (dpeaa)DE-He213 Compression ratio (dpeaa)DE-He213 Image quality (dpeaa)DE-He213 Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 79(2020), 35-36 vom: 14. Juli, Seite 26345-26368 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:79 year:2020 number:35-36 day:14 month:07 pages:26345-26368 https://dx.doi.org/10.1007/s11042-020-09256-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE 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 54.87 ASE AR 79 2020 35-36 14 07 26345-26368
allfields_unstemmed	10.1007/s11042-020-09256-z doi (DE-627)SPR04074034X (SPR)s11042-020-09256-z-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Nandi, Utpal verfasserin aut Fractal image compression with adaptive quadtree partitioning and non-linear affine map 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Fractal image compression techniques are now very popular for its high compression rates and resolution independence property. However, the qualities of decoded images of the existing techniques are not satisfactory. An adaptive partitioning scheme can improve the image quality significantly. These existing adaptive techniques use linear affine maps during encoding that have limited pixel intensity approximation ability. In order to increase the image quality further, non-linear affine maps can be used that generalizes the pixel intensity approximation and generates much better approximation. Here, a fractal based technique for image compression using non-linear contractive affine maps has been proposed that applies adaptive quadtree partitioning to partition image in a context dependent way to enhance decoded image quality. The technique partitions twice an image to be compressed to obtain collection of ranges and domains and finds the highest matching non-linear affine transformed domain of each range. The corresponding affine parameters are kept in the compressed file. However, a range may be broken into sub-ranges using adaptive quadtree partitioning for unavailability of enough matching domains and repeat the same on those. The comparative results show that the proposed technique greatly improves the decoded image quality than existing techniques and also maintains the high compression ratios. Two variants have also been proposed that improve compression ratio of the proposed technique without any degradation of image quality using loss-less coding. Fractal compression (dpeaa)DE-He213 Adaptive quadtree partition (dpeaa)DE-He213 Affine map (dpeaa)DE-He213 Non-linear affine map (dpeaa)DE-He213 Contractive transform (dpeaa)DE-He213 Compression ratio (dpeaa)DE-He213 Image quality (dpeaa)DE-He213 Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 79(2020), 35-36 vom: 14. Juli, Seite 26345-26368 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:79 year:2020 number:35-36 day:14 month:07 pages:26345-26368 https://dx.doi.org/10.1007/s11042-020-09256-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE 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 54.87 ASE AR 79 2020 35-36 14 07 26345-26368
allfieldsGer	10.1007/s11042-020-09256-z doi (DE-627)SPR04074034X (SPR)s11042-020-09256-z-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Nandi, Utpal verfasserin aut Fractal image compression with adaptive quadtree partitioning and non-linear affine map 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Fractal image compression techniques are now very popular for its high compression rates and resolution independence property. However, the qualities of decoded images of the existing techniques are not satisfactory. An adaptive partitioning scheme can improve the image quality significantly. These existing adaptive techniques use linear affine maps during encoding that have limited pixel intensity approximation ability. In order to increase the image quality further, non-linear affine maps can be used that generalizes the pixel intensity approximation and generates much better approximation. Here, a fractal based technique for image compression using non-linear contractive affine maps has been proposed that applies adaptive quadtree partitioning to partition image in a context dependent way to enhance decoded image quality. The technique partitions twice an image to be compressed to obtain collection of ranges and domains and finds the highest matching non-linear affine transformed domain of each range. The corresponding affine parameters are kept in the compressed file. However, a range may be broken into sub-ranges using adaptive quadtree partitioning for unavailability of enough matching domains and repeat the same on those. The comparative results show that the proposed technique greatly improves the decoded image quality than existing techniques and also maintains the high compression ratios. Two variants have also been proposed that improve compression ratio of the proposed technique without any degradation of image quality using loss-less coding. Fractal compression (dpeaa)DE-He213 Adaptive quadtree partition (dpeaa)DE-He213 Affine map (dpeaa)DE-He213 Non-linear affine map (dpeaa)DE-He213 Contractive transform (dpeaa)DE-He213 Compression ratio (dpeaa)DE-He213 Image quality (dpeaa)DE-He213 Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 79(2020), 35-36 vom: 14. Juli, Seite 26345-26368 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:79 year:2020 number:35-36 day:14 month:07 pages:26345-26368 https://dx.doi.org/10.1007/s11042-020-09256-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE 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 54.87 ASE AR 79 2020 35-36 14 07 26345-26368
allfieldsSound	10.1007/s11042-020-09256-z doi (DE-627)SPR04074034X (SPR)s11042-020-09256-z-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Nandi, Utpal verfasserin aut Fractal image compression with adaptive quadtree partitioning and non-linear affine map 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Fractal image compression techniques are now very popular for its high compression rates and resolution independence property. However, the qualities of decoded images of the existing techniques are not satisfactory. An adaptive partitioning scheme can improve the image quality significantly. These existing adaptive techniques use linear affine maps during encoding that have limited pixel intensity approximation ability. In order to increase the image quality further, non-linear affine maps can be used that generalizes the pixel intensity approximation and generates much better approximation. Here, a fractal based technique for image compression using non-linear contractive affine maps has been proposed that applies adaptive quadtree partitioning to partition image in a context dependent way to enhance decoded image quality. The technique partitions twice an image to be compressed to obtain collection of ranges and domains and finds the highest matching non-linear affine transformed domain of each range. The corresponding affine parameters are kept in the compressed file. However, a range may be broken into sub-ranges using adaptive quadtree partitioning for unavailability of enough matching domains and repeat the same on those. The comparative results show that the proposed technique greatly improves the decoded image quality than existing techniques and also maintains the high compression ratios. Two variants have also been proposed that improve compression ratio of the proposed technique without any degradation of image quality using loss-less coding. Fractal compression (dpeaa)DE-He213 Adaptive quadtree partition (dpeaa)DE-He213 Affine map (dpeaa)DE-He213 Non-linear affine map (dpeaa)DE-He213 Contractive transform (dpeaa)DE-He213 Compression ratio (dpeaa)DE-He213 Image quality (dpeaa)DE-He213 Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 79(2020), 35-36 vom: 14. Juli, Seite 26345-26368 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:79 year:2020 number:35-36 day:14 month:07 pages:26345-26368 https://dx.doi.org/10.1007/s11042-020-09256-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE 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 54.87 ASE AR 79 2020 35-36 14 07 26345-26368
language	English
source	Enthalten in Multimedia tools and applications 79(2020), 35-36 vom: 14. Juli, Seite 26345-26368 volume:79 year:2020 number:35-36 day:14 month:07 pages:26345-26368
sourceStr	Enthalten in Multimedia tools and applications 79(2020), 35-36 vom: 14. Juli, Seite 26345-26368 volume:79 year:2020 number:35-36 day:14 month:07 pages:26345-26368
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Fractal compression Adaptive quadtree partition Affine map Non-linear affine map Contractive transform Compression ratio Image quality
dewey-raw	070
isfreeaccess_bool	false
container_title	Multimedia tools and applications
authorswithroles_txt_mv	Nandi, Utpal @@aut@@
publishDateDaySort_date	2020-07-14T00:00:00Z
hierarchy_top_id	27135030X
dewey-sort	270
id	SPR04074034X
language_de	englisch
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However, the qualities of decoded images of the existing techniques are not satisfactory. An adaptive partitioning scheme can improve the image quality significantly. These existing adaptive techniques use linear affine maps during encoding that have limited pixel intensity approximation ability. In order to increase the image quality further, non-linear affine maps can be used that generalizes the pixel intensity approximation and generates much better approximation. Here, a fractal based technique for image compression using non-linear contractive affine maps has been proposed that applies adaptive quadtree partitioning to partition image in a context dependent way to enhance decoded image quality. The technique partitions twice an image to be compressed to obtain collection of ranges and domains and finds the highest matching non-linear affine transformed domain of each range. The corresponding affine parameters are kept in the compressed file. 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author	Nandi, Utpal
spellingShingle	Nandi, Utpal ddc 070 bkl 54.87 misc Fractal compression misc Adaptive quadtree partition misc Affine map misc Non-linear affine map misc Contractive transform misc Compression ratio misc Image quality Fractal image compression with adaptive quadtree partitioning and non-linear affine map
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topic_title	070 004 ASE 54.87 bkl Fractal image compression with adaptive quadtree partitioning and non-linear affine map Fractal compression (dpeaa)DE-He213 Adaptive quadtree partition (dpeaa)DE-He213 Affine map (dpeaa)DE-He213 Non-linear affine map (dpeaa)DE-He213 Contractive transform (dpeaa)DE-He213 Compression ratio (dpeaa)DE-He213 Image quality (dpeaa)DE-He213
topic	ddc 070 bkl 54.87 misc Fractal compression misc Adaptive quadtree partition misc Affine map misc Non-linear affine map misc Contractive transform misc Compression ratio misc Image quality
topic_unstemmed	ddc 070 bkl 54.87 misc Fractal compression misc Adaptive quadtree partition misc Affine map misc Non-linear affine map misc Contractive transform misc Compression ratio misc Image quality
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title	Fractal image compression with adaptive quadtree partitioning and non-linear affine map
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title_full	Fractal image compression with adaptive quadtree partitioning and non-linear affine map
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title_sort	fractal image compression with adaptive quadtree partitioning and non-linear affine map
title_auth	Fractal image compression with adaptive quadtree partitioning and non-linear affine map
abstract	Abstract Fractal image compression techniques are now very popular for its high compression rates and resolution independence property. However, the qualities of decoded images of the existing techniques are not satisfactory. An adaptive partitioning scheme can improve the image quality significantly. These existing adaptive techniques use linear affine maps during encoding that have limited pixel intensity approximation ability. In order to increase the image quality further, non-linear affine maps can be used that generalizes the pixel intensity approximation and generates much better approximation. Here, a fractal based technique for image compression using non-linear contractive affine maps has been proposed that applies adaptive quadtree partitioning to partition image in a context dependent way to enhance decoded image quality. The technique partitions twice an image to be compressed to obtain collection of ranges and domains and finds the highest matching non-linear affine transformed domain of each range. The corresponding affine parameters are kept in the compressed file. However, a range may be broken into sub-ranges using adaptive quadtree partitioning for unavailability of enough matching domains and repeat the same on those. The comparative results show that the proposed technique greatly improves the decoded image quality than existing techniques and also maintains the high compression ratios. Two variants have also been proposed that improve compression ratio of the proposed technique without any degradation of image quality using loss-less coding.
abstractGer	Abstract Fractal image compression techniques are now very popular for its high compression rates and resolution independence property. However, the qualities of decoded images of the existing techniques are not satisfactory. An adaptive partitioning scheme can improve the image quality significantly. These existing adaptive techniques use linear affine maps during encoding that have limited pixel intensity approximation ability. In order to increase the image quality further, non-linear affine maps can be used that generalizes the pixel intensity approximation and generates much better approximation. Here, a fractal based technique for image compression using non-linear contractive affine maps has been proposed that applies adaptive quadtree partitioning to partition image in a context dependent way to enhance decoded image quality. The technique partitions twice an image to be compressed to obtain collection of ranges and domains and finds the highest matching non-linear affine transformed domain of each range. The corresponding affine parameters are kept in the compressed file. However, a range may be broken into sub-ranges using adaptive quadtree partitioning for unavailability of enough matching domains and repeat the same on those. The comparative results show that the proposed technique greatly improves the decoded image quality than existing techniques and also maintains the high compression ratios. Two variants have also been proposed that improve compression ratio of the proposed technique without any degradation of image quality using loss-less coding.
abstract_unstemmed	Abstract Fractal image compression techniques are now very popular for its high compression rates and resolution independence property. However, the qualities of decoded images of the existing techniques are not satisfactory. An adaptive partitioning scheme can improve the image quality significantly. These existing adaptive techniques use linear affine maps during encoding that have limited pixel intensity approximation ability. In order to increase the image quality further, non-linear affine maps can be used that generalizes the pixel intensity approximation and generates much better approximation. Here, a fractal based technique for image compression using non-linear contractive affine maps has been proposed that applies adaptive quadtree partitioning to partition image in a context dependent way to enhance decoded image quality. The technique partitions twice an image to be compressed to obtain collection of ranges and domains and finds the highest matching non-linear affine transformed domain of each range. The corresponding affine parameters are kept in the compressed file. However, a range may be broken into sub-ranges using adaptive quadtree partitioning for unavailability of enough matching domains and repeat the same on those. The comparative results show that the proposed technique greatly improves the decoded image quality than existing techniques and also maintains the high compression ratios. Two variants have also been proposed that improve compression ratio of the proposed technique without any degradation of image quality using loss-less coding.
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title_short	Fractal image compression with adaptive quadtree partitioning and non-linear affine map
url	https://dx.doi.org/10.1007/s11042-020-09256-z
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up_date	2024-07-03T17:57:33.356Z
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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">SPR04074034X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111024635.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11042-020-09256-z</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR04074034X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11042-020-09256-z-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">070</subfield><subfield code="a">004</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.87</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="0"><subfield code="a">Fractal image compression with adaptive quadtree partitioning and non-linear affine map</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</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 image compression techniques are now very popular for its high compression rates and resolution independence property. However, the qualities of decoded images of the existing techniques are not satisfactory. An adaptive partitioning scheme can improve the image quality significantly. These existing adaptive techniques use linear affine maps during encoding that have limited pixel intensity approximation ability. In order to increase the image quality further, non-linear affine maps can be used that generalizes the pixel intensity approximation and generates much better approximation. Here, a fractal based technique for image compression using non-linear contractive affine maps has been proposed that applies adaptive quadtree partitioning to partition image in a context dependent way to enhance decoded image quality. The technique partitions twice an image to be compressed to obtain collection of ranges and domains and finds the highest matching non-linear affine transformed domain of each range. The corresponding affine parameters are kept in the compressed file. However, a range may be broken into sub-ranges using adaptive quadtree partitioning for unavailability of enough matching domains and repeat the same on those. The comparative results show that the proposed technique greatly improves the decoded image quality than existing techniques and also maintains the high compression ratios. Two variants have also been proposed that improve compression ratio of the proposed technique without any degradation of image quality using loss-less coding.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fractal compression</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Adaptive quadtree partition</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Affine map</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Non-linear affine map</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Contractive transform</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Compression ratio</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Image quality</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Multimedia tools and applications</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995</subfield><subfield code="g">79(2020), 35-36 vom: 14. 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