Image information loss estimation of video stream based on improved SPIHT algorithm
Abstract With the continuous development of multimedia technology, digital image and video data show a massive growth. Many files containing image and video data often need to be exchanged between different users and systems, which requires effective methods to store and transfer these files. The ap...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhai, Lijie [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 81(2022), 25 vom: 18. März, Seite 36275-36291 |
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| Übergeordnetes Werk: |
volume:81 ; year:2022 ; number:25 ; day:18 ; month:03 ; pages:36275-36291 |
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| DOI / URN: |
10.1007/s11042-021-11572-x |
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SPR048199559 |
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| 520 | |a Abstract With the continuous development of multimedia technology, digital image and video data show a massive growth. Many files containing image and video data often need to be exchanged between different users and systems, which requires effective methods to store and transfer these files. The application of video image compression and coding technology is more and more extensive. Its outstanding problem is the large amount of data, requiring a lot of transmission bandwidth and high real-time. The traditional Set Partitioning in Hierarchical Trees (SPIHT) algorithm has the disadvantages of repeated operation and large storage. In order to ensure the real-time image transmission, obtain high compression ratio and reduce the loss of image information, a new improved algorithm for image compression technology is proposed. The improved algorithm MSPIHT (modified SPIHT) introduces fast lifting wavelet transform to improve the transformation process and threshold optimization to improve the quality of real-time image restoration. Simulation results show that the improved method can reduce the loss of video stream image information, and has good real-time performance. | ||
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10.1007/s11042-021-11572-x doi (DE-627)SPR048199559 (SPR)s11042-021-11572-x-e DE-627 ger DE-627 rakwb eng Zhai, Lijie verfasserin (orcid)0000-0003-1831-8626 aut Image information loss estimation of video stream based on improved SPIHT algorithm 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract With the continuous development of multimedia technology, digital image and video data show a massive growth. Many files containing image and video data often need to be exchanged between different users and systems, which requires effective methods to store and transfer these files. The application of video image compression and coding technology is more and more extensive. Its outstanding problem is the large amount of data, requiring a lot of transmission bandwidth and high real-time. The traditional Set Partitioning in Hierarchical Trees (SPIHT) algorithm has the disadvantages of repeated operation and large storage. In order to ensure the real-time image transmission, obtain high compression ratio and reduce the loss of image information, a new improved algorithm for image compression technology is proposed. The improved algorithm MSPIHT (modified SPIHT) introduces fast lifting wavelet transform to improve the transformation process and threshold optimization to improve the quality of real-time image restoration. Simulation results show that the improved method can reduce the loss of video stream image information, and has good real-time performance. Wavelet transform (dpeaa)DE-He213 Information loss (dpeaa)DE-He213 Threshold optimization (dpeaa)DE-He213 SPIHT (dpeaa)DE-He213 Sheng, Duanhai aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 25 vom: 18. März, Seite 36275-36291 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:25 day:18 month:03 pages:36275-36291 https://dx.doi.org/10.1007/s11042-021-11572-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81 2022 25 18 03 36275-36291 |
| spelling |
10.1007/s11042-021-11572-x doi (DE-627)SPR048199559 (SPR)s11042-021-11572-x-e DE-627 ger DE-627 rakwb eng Zhai, Lijie verfasserin (orcid)0000-0003-1831-8626 aut Image information loss estimation of video stream based on improved SPIHT algorithm 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract With the continuous development of multimedia technology, digital image and video data show a massive growth. Many files containing image and video data often need to be exchanged between different users and systems, which requires effective methods to store and transfer these files. The application of video image compression and coding technology is more and more extensive. Its outstanding problem is the large amount of data, requiring a lot of transmission bandwidth and high real-time. The traditional Set Partitioning in Hierarchical Trees (SPIHT) algorithm has the disadvantages of repeated operation and large storage. In order to ensure the real-time image transmission, obtain high compression ratio and reduce the loss of image information, a new improved algorithm for image compression technology is proposed. The improved algorithm MSPIHT (modified SPIHT) introduces fast lifting wavelet transform to improve the transformation process and threshold optimization to improve the quality of real-time image restoration. Simulation results show that the improved method can reduce the loss of video stream image information, and has good real-time performance. Wavelet transform (dpeaa)DE-He213 Information loss (dpeaa)DE-He213 Threshold optimization (dpeaa)DE-He213 SPIHT (dpeaa)DE-He213 Sheng, Duanhai aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 25 vom: 18. März, Seite 36275-36291 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:25 day:18 month:03 pages:36275-36291 https://dx.doi.org/10.1007/s11042-021-11572-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81 2022 25 18 03 36275-36291 |
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10.1007/s11042-021-11572-x doi (DE-627)SPR048199559 (SPR)s11042-021-11572-x-e DE-627 ger DE-627 rakwb eng Zhai, Lijie verfasserin (orcid)0000-0003-1831-8626 aut Image information loss estimation of video stream based on improved SPIHT algorithm 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract With the continuous development of multimedia technology, digital image and video data show a massive growth. Many files containing image and video data often need to be exchanged between different users and systems, which requires effective methods to store and transfer these files. The application of video image compression and coding technology is more and more extensive. Its outstanding problem is the large amount of data, requiring a lot of transmission bandwidth and high real-time. The traditional Set Partitioning in Hierarchical Trees (SPIHT) algorithm has the disadvantages of repeated operation and large storage. In order to ensure the real-time image transmission, obtain high compression ratio and reduce the loss of image information, a new improved algorithm for image compression technology is proposed. The improved algorithm MSPIHT (modified SPIHT) introduces fast lifting wavelet transform to improve the transformation process and threshold optimization to improve the quality of real-time image restoration. Simulation results show that the improved method can reduce the loss of video stream image information, and has good real-time performance. Wavelet transform (dpeaa)DE-He213 Information loss (dpeaa)DE-He213 Threshold optimization (dpeaa)DE-He213 SPIHT (dpeaa)DE-He213 Sheng, Duanhai aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 25 vom: 18. März, Seite 36275-36291 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:25 day:18 month:03 pages:36275-36291 https://dx.doi.org/10.1007/s11042-021-11572-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81 2022 25 18 03 36275-36291 |
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10.1007/s11042-021-11572-x doi (DE-627)SPR048199559 (SPR)s11042-021-11572-x-e DE-627 ger DE-627 rakwb eng Zhai, Lijie verfasserin (orcid)0000-0003-1831-8626 aut Image information loss estimation of video stream based on improved SPIHT algorithm 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract With the continuous development of multimedia technology, digital image and video data show a massive growth. Many files containing image and video data often need to be exchanged between different users and systems, which requires effective methods to store and transfer these files. The application of video image compression and coding technology is more and more extensive. Its outstanding problem is the large amount of data, requiring a lot of transmission bandwidth and high real-time. The traditional Set Partitioning in Hierarchical Trees (SPIHT) algorithm has the disadvantages of repeated operation and large storage. In order to ensure the real-time image transmission, obtain high compression ratio and reduce the loss of image information, a new improved algorithm for image compression technology is proposed. The improved algorithm MSPIHT (modified SPIHT) introduces fast lifting wavelet transform to improve the transformation process and threshold optimization to improve the quality of real-time image restoration. Simulation results show that the improved method can reduce the loss of video stream image information, and has good real-time performance. Wavelet transform (dpeaa)DE-He213 Information loss (dpeaa)DE-He213 Threshold optimization (dpeaa)DE-He213 SPIHT (dpeaa)DE-He213 Sheng, Duanhai aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 25 vom: 18. März, Seite 36275-36291 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:25 day:18 month:03 pages:36275-36291 https://dx.doi.org/10.1007/s11042-021-11572-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81 2022 25 18 03 36275-36291 |
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Zhai, Lijie |
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Image information loss estimation of video stream based on improved SPIHT algorithm Wavelet transform (dpeaa)DE-He213 Information loss (dpeaa)DE-He213 Threshold optimization (dpeaa)DE-He213 SPIHT (dpeaa)DE-He213 |
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image information loss estimation of video stream based on improved spiht algorithm |
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Image information loss estimation of video stream based on improved SPIHT algorithm |
| abstract |
Abstract With the continuous development of multimedia technology, digital image and video data show a massive growth. Many files containing image and video data often need to be exchanged between different users and systems, which requires effective methods to store and transfer these files. The application of video image compression and coding technology is more and more extensive. Its outstanding problem is the large amount of data, requiring a lot of transmission bandwidth and high real-time. The traditional Set Partitioning in Hierarchical Trees (SPIHT) algorithm has the disadvantages of repeated operation and large storage. In order to ensure the real-time image transmission, obtain high compression ratio and reduce the loss of image information, a new improved algorithm for image compression technology is proposed. The improved algorithm MSPIHT (modified SPIHT) introduces fast lifting wavelet transform to improve the transformation process and threshold optimization to improve the quality of real-time image restoration. Simulation results show that the improved method can reduce the loss of video stream image information, and has good real-time performance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstractGer |
Abstract With the continuous development of multimedia technology, digital image and video data show a massive growth. Many files containing image and video data often need to be exchanged between different users and systems, which requires effective methods to store and transfer these files. The application of video image compression and coding technology is more and more extensive. Its outstanding problem is the large amount of data, requiring a lot of transmission bandwidth and high real-time. The traditional Set Partitioning in Hierarchical Trees (SPIHT) algorithm has the disadvantages of repeated operation and large storage. In order to ensure the real-time image transmission, obtain high compression ratio and reduce the loss of image information, a new improved algorithm for image compression technology is proposed. The improved algorithm MSPIHT (modified SPIHT) introduces fast lifting wavelet transform to improve the transformation process and threshold optimization to improve the quality of real-time image restoration. Simulation results show that the improved method can reduce the loss of video stream image information, and has good real-time performance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract With the continuous development of multimedia technology, digital image and video data show a massive growth. Many files containing image and video data often need to be exchanged between different users and systems, which requires effective methods to store and transfer these files. The application of video image compression and coding technology is more and more extensive. Its outstanding problem is the large amount of data, requiring a lot of transmission bandwidth and high real-time. The traditional Set Partitioning in Hierarchical Trees (SPIHT) algorithm has the disadvantages of repeated operation and large storage. In order to ensure the real-time image transmission, obtain high compression ratio and reduce the loss of image information, a new improved algorithm for image compression technology is proposed. The improved algorithm MSPIHT (modified SPIHT) introduces fast lifting wavelet transform to improve the transformation process and threshold optimization to improve the quality of real-time image restoration. Simulation results show that the improved method can reduce the loss of video stream image information, and has good real-time performance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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Image information loss estimation of video stream based on improved SPIHT algorithm |
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Sheng, Duanhai |
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10.1007/s11042-021-11572-x |
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