RETRACTED ARTICLE: Research on multimedia play mode and image optimization based on compensation factor adaptive model
Abstract When traditional multimedia network video image is compressed and transmitted to compensate, because of the different loss of video image features in the acquisition process, the error of compressed transmission compensation is large and the efficiency is low. Firstly, the NLMS algorithm an...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Liu, Yuling [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2019 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 79(2019), 13-14 vom: 02. Apr., Seite 9315-9330 |
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| Übergeordnetes Werk: |
volume:79 ; year:2019 ; number:13-14 ; day:02 ; month:04 ; pages:9315-9330 |
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| DOI / URN: |
10.1007/s11042-019-7478-7 |
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| 520 | |a Abstract When traditional multimedia network video image is compressed and transmitted to compensate, because of the different loss of video image features in the acquisition process, the error of compressed transmission compensation is large and the efficiency is low. Firstly, the NLMS algorithm and the improved NLMS algorithm are analyzed. To solve the problem that the compensation factor in the algorithm is too large due to the severe network shake, the NLMS algorithm is further improved by adaptively adjusting the compensation factor coefficient with the change of the network and the prediction error. The research shows that the multimedia playback mode and the image optimization system software structure based on the compensation factor adaptive model realize the dynamic adaptation of the system to various network conditions and the image optimization function during video playback by adopting the bandwidth adaptive strategy and method.. The conclusion shows that in the multimedia network video image compression transmission, the improved compensation method has higher performance in multimedia network video image optimization and real-time compression transmission compensation, which has certain advantages compared with the traditional compensation method. | ||
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| 650 | 4 | |a Image optimization |7 (dpeaa)DE-He213 | |
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10.1007/s11042-019-7478-7 doi (DE-627)SPR039338819 (SPR)s11042-019-7478-7-e DE-627 ger DE-627 rakwb eng Liu, Yuling verfasserin aut RETRACTED ARTICLE: Research on multimedia play mode and image optimization based on compensation factor adaptive model 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract When traditional multimedia network video image is compressed and transmitted to compensate, because of the different loss of video image features in the acquisition process, the error of compressed transmission compensation is large and the efficiency is low. Firstly, the NLMS algorithm and the improved NLMS algorithm are analyzed. To solve the problem that the compensation factor in the algorithm is too large due to the severe network shake, the NLMS algorithm is further improved by adaptively adjusting the compensation factor coefficient with the change of the network and the prediction error. The research shows that the multimedia playback mode and the image optimization system software structure based on the compensation factor adaptive model realize the dynamic adaptation of the system to various network conditions and the image optimization function during video playback by adopting the bandwidth adaptive strategy and method.. The conclusion shows that in the multimedia network video image compression transmission, the improved compensation method has higher performance in multimedia network video image optimization and real-time compression transmission compensation, which has certain advantages compared with the traditional compensation method. Compensation factor self-adaptation (dpeaa)DE-He213 Multimedia playback (dpeaa)DE-He213 Image optimization (dpeaa)DE-He213 Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 79(2019), 13-14 vom: 02. Apr., Seite 9315-9330 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:79 year:2019 number:13-14 day:02 month:04 pages:9315-9330 https://dx.doi.org/10.1007/s11042-019-7478-7 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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 79 2019 13-14 02 04 9315-9330 |
| spelling |
10.1007/s11042-019-7478-7 doi (DE-627)SPR039338819 (SPR)s11042-019-7478-7-e DE-627 ger DE-627 rakwb eng Liu, Yuling verfasserin aut RETRACTED ARTICLE: Research on multimedia play mode and image optimization based on compensation factor adaptive model 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract When traditional multimedia network video image is compressed and transmitted to compensate, because of the different loss of video image features in the acquisition process, the error of compressed transmission compensation is large and the efficiency is low. Firstly, the NLMS algorithm and the improved NLMS algorithm are analyzed. To solve the problem that the compensation factor in the algorithm is too large due to the severe network shake, the NLMS algorithm is further improved by adaptively adjusting the compensation factor coefficient with the change of the network and the prediction error. The research shows that the multimedia playback mode and the image optimization system software structure based on the compensation factor adaptive model realize the dynamic adaptation of the system to various network conditions and the image optimization function during video playback by adopting the bandwidth adaptive strategy and method.. The conclusion shows that in the multimedia network video image compression transmission, the improved compensation method has higher performance in multimedia network video image optimization and real-time compression transmission compensation, which has certain advantages compared with the traditional compensation method. Compensation factor self-adaptation (dpeaa)DE-He213 Multimedia playback (dpeaa)DE-He213 Image optimization (dpeaa)DE-He213 Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 79(2019), 13-14 vom: 02. Apr., Seite 9315-9330 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:79 year:2019 number:13-14 day:02 month:04 pages:9315-9330 https://dx.doi.org/10.1007/s11042-019-7478-7 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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 79 2019 13-14 02 04 9315-9330 |
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10.1007/s11042-019-7478-7 doi (DE-627)SPR039338819 (SPR)s11042-019-7478-7-e DE-627 ger DE-627 rakwb eng Liu, Yuling verfasserin aut RETRACTED ARTICLE: Research on multimedia play mode and image optimization based on compensation factor adaptive model 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract When traditional multimedia network video image is compressed and transmitted to compensate, because of the different loss of video image features in the acquisition process, the error of compressed transmission compensation is large and the efficiency is low. Firstly, the NLMS algorithm and the improved NLMS algorithm are analyzed. To solve the problem that the compensation factor in the algorithm is too large due to the severe network shake, the NLMS algorithm is further improved by adaptively adjusting the compensation factor coefficient with the change of the network and the prediction error. The research shows that the multimedia playback mode and the image optimization system software structure based on the compensation factor adaptive model realize the dynamic adaptation of the system to various network conditions and the image optimization function during video playback by adopting the bandwidth adaptive strategy and method.. The conclusion shows that in the multimedia network video image compression transmission, the improved compensation method has higher performance in multimedia network video image optimization and real-time compression transmission compensation, which has certain advantages compared with the traditional compensation method. Compensation factor self-adaptation (dpeaa)DE-He213 Multimedia playback (dpeaa)DE-He213 Image optimization (dpeaa)DE-He213 Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 79(2019), 13-14 vom: 02. Apr., Seite 9315-9330 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:79 year:2019 number:13-14 day:02 month:04 pages:9315-9330 https://dx.doi.org/10.1007/s11042-019-7478-7 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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 79 2019 13-14 02 04 9315-9330 |
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10.1007/s11042-019-7478-7 doi (DE-627)SPR039338819 (SPR)s11042-019-7478-7-e DE-627 ger DE-627 rakwb eng Liu, Yuling verfasserin aut RETRACTED ARTICLE: Research on multimedia play mode and image optimization based on compensation factor adaptive model 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract When traditional multimedia network video image is compressed and transmitted to compensate, because of the different loss of video image features in the acquisition process, the error of compressed transmission compensation is large and the efficiency is low. Firstly, the NLMS algorithm and the improved NLMS algorithm are analyzed. To solve the problem that the compensation factor in the algorithm is too large due to the severe network shake, the NLMS algorithm is further improved by adaptively adjusting the compensation factor coefficient with the change of the network and the prediction error. The research shows that the multimedia playback mode and the image optimization system software structure based on the compensation factor adaptive model realize the dynamic adaptation of the system to various network conditions and the image optimization function during video playback by adopting the bandwidth adaptive strategy and method.. The conclusion shows that in the multimedia network video image compression transmission, the improved compensation method has higher performance in multimedia network video image optimization and real-time compression transmission compensation, which has certain advantages compared with the traditional compensation method. Compensation factor self-adaptation (dpeaa)DE-He213 Multimedia playback (dpeaa)DE-He213 Image optimization (dpeaa)DE-He213 Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 79(2019), 13-14 vom: 02. Apr., Seite 9315-9330 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:79 year:2019 number:13-14 day:02 month:04 pages:9315-9330 https://dx.doi.org/10.1007/s11042-019-7478-7 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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 79 2019 13-14 02 04 9315-9330 |
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10.1007/s11042-019-7478-7 doi (DE-627)SPR039338819 (SPR)s11042-019-7478-7-e DE-627 ger DE-627 rakwb eng Liu, Yuling verfasserin aut RETRACTED ARTICLE: Research on multimedia play mode and image optimization based on compensation factor adaptive model 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract When traditional multimedia network video image is compressed and transmitted to compensate, because of the different loss of video image features in the acquisition process, the error of compressed transmission compensation is large and the efficiency is low. Firstly, the NLMS algorithm and the improved NLMS algorithm are analyzed. To solve the problem that the compensation factor in the algorithm is too large due to the severe network shake, the NLMS algorithm is further improved by adaptively adjusting the compensation factor coefficient with the change of the network and the prediction error. The research shows that the multimedia playback mode and the image optimization system software structure based on the compensation factor adaptive model realize the dynamic adaptation of the system to various network conditions and the image optimization function during video playback by adopting the bandwidth adaptive strategy and method.. The conclusion shows that in the multimedia network video image compression transmission, the improved compensation method has higher performance in multimedia network video image optimization and real-time compression transmission compensation, which has certain advantages compared with the traditional compensation method. Compensation factor self-adaptation (dpeaa)DE-He213 Multimedia playback (dpeaa)DE-He213 Image optimization (dpeaa)DE-He213 Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 79(2019), 13-14 vom: 02. Apr., Seite 9315-9330 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:79 year:2019 number:13-14 day:02 month:04 pages:9315-9330 https://dx.doi.org/10.1007/s11042-019-7478-7 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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 79 2019 13-14 02 04 9315-9330 |
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retracted article: research on multimedia play mode and image optimization based on compensation factor adaptive model |
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RETRACTED ARTICLE: Research on multimedia play mode and image optimization based on compensation factor adaptive model |
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Abstract When traditional multimedia network video image is compressed and transmitted to compensate, because of the different loss of video image features in the acquisition process, the error of compressed transmission compensation is large and the efficiency is low. Firstly, the NLMS algorithm and the improved NLMS algorithm are analyzed. To solve the problem that the compensation factor in the algorithm is too large due to the severe network shake, the NLMS algorithm is further improved by adaptively adjusting the compensation factor coefficient with the change of the network and the prediction error. The research shows that the multimedia playback mode and the image optimization system software structure based on the compensation factor adaptive model realize the dynamic adaptation of the system to various network conditions and the image optimization function during video playback by adopting the bandwidth adaptive strategy and method.. The conclusion shows that in the multimedia network video image compression transmission, the improved compensation method has higher performance in multimedia network video image optimization and real-time compression transmission compensation, which has certain advantages compared with the traditional compensation method. © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
| abstractGer |
Abstract When traditional multimedia network video image is compressed and transmitted to compensate, because of the different loss of video image features in the acquisition process, the error of compressed transmission compensation is large and the efficiency is low. Firstly, the NLMS algorithm and the improved NLMS algorithm are analyzed. To solve the problem that the compensation factor in the algorithm is too large due to the severe network shake, the NLMS algorithm is further improved by adaptively adjusting the compensation factor coefficient with the change of the network and the prediction error. The research shows that the multimedia playback mode and the image optimization system software structure based on the compensation factor adaptive model realize the dynamic adaptation of the system to various network conditions and the image optimization function during video playback by adopting the bandwidth adaptive strategy and method.. The conclusion shows that in the multimedia network video image compression transmission, the improved compensation method has higher performance in multimedia network video image optimization and real-time compression transmission compensation, which has certain advantages compared with the traditional compensation method. © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
| abstract_unstemmed |
Abstract When traditional multimedia network video image is compressed and transmitted to compensate, because of the different loss of video image features in the acquisition process, the error of compressed transmission compensation is large and the efficiency is low. Firstly, the NLMS algorithm and the improved NLMS algorithm are analyzed. To solve the problem that the compensation factor in the algorithm is too large due to the severe network shake, the NLMS algorithm is further improved by adaptively adjusting the compensation factor coefficient with the change of the network and the prediction error. The research shows that the multimedia playback mode and the image optimization system software structure based on the compensation factor adaptive model realize the dynamic adaptation of the system to various network conditions and the image optimization function during video playback by adopting the bandwidth adaptive strategy and method.. The conclusion shows that in the multimedia network video image compression transmission, the improved compensation method has higher performance in multimedia network video image optimization and real-time compression transmission compensation, which has certain advantages compared with the traditional compensation method. © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
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