Neural texture transfer assisted video coding with adaptive up-sampling
Deep learning techniques have been extensively investigated for the purpose of further increasing the efficiency of traditional video compression. Some deep learning techniques for down/up-sampling-based video coding were found to be especially effective when the bandwidth or storage is limited. Exi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yu, Li [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
|---|
| Schlagwörter: |
High-efficiency video coding (HEVC) |
|---|
| Übergeordnetes Werk: |
Enthalten in: Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis - Zeng, C. ELSEVIER, 2014, theory, techniques & applications, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:107 ; year:2022 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.image.2022.116754 |
|---|
| Katalog-ID: |
ELV058357971 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV058357971 | ||
| 003 | DE-627 | ||
| 005 | 20230626050723.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 220808s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.image.2022.116754 |2 doi | |
| 028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001927.pica |
| 035 | |a (DE-627)ELV058357971 | ||
| 035 | |a (ELSEVIER)S0923-5965(22)00077-7 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 610 |q VZ |
| 082 | 0 | 4 | |a 660 |a 620 |q VZ |
| 084 | |a 52.56 |2 bkl | ||
| 100 | 1 | |a Yu, Li |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Neural texture transfer assisted video coding with adaptive up-sampling |
| 264 | 1 | |c 2022transfer abstract | |
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a Deep learning techniques have been extensively investigated for the purpose of further increasing the efficiency of traditional video compression. Some deep learning techniques for down/up-sampling-based video coding were found to be especially effective when the bandwidth or storage is limited. Existing works mainly differ in the super-resolution models used. Some works simply use a single image super-resolution model, ignoring the rich information in the correlation between video frames, while others explore the correlation between frames by simply concatenating the features across adjacent frames. This, however, may fail when the textures are not well aligned. In this paper, we propose to utilize neural texture transfer which exploits the semantic correlation between frames and is able to explore the correlated information even when the textures are not aligned. Meanwhile, an adaptive group of pictures (GOP) method is proposed to automatically decide whether a frame should be down-sampled or not. Experimental results show that the proposed method outperforms the standard HEVC and state-of-the-art methods under different compression configurations. When compared to standard HEVC, the BD-rate (PSNR) and BD-rate (SSIM) of the proposed method are up to -19.1% and -26.5%, respectively. | ||
| 520 | |a Deep learning techniques have been extensively investigated for the purpose of further increasing the efficiency of traditional video compression. Some deep learning techniques for down/up-sampling-based video coding were found to be especially effective when the bandwidth or storage is limited. Existing works mainly differ in the super-resolution models used. Some works simply use a single image super-resolution model, ignoring the rich information in the correlation between video frames, while others explore the correlation between frames by simply concatenating the features across adjacent frames. This, however, may fail when the textures are not well aligned. In this paper, we propose to utilize neural texture transfer which exploits the semantic correlation between frames and is able to explore the correlated information even when the textures are not aligned. Meanwhile, an adaptive group of pictures (GOP) method is proposed to automatically decide whether a frame should be down-sampled or not. Experimental results show that the proposed method outperforms the standard HEVC and state-of-the-art methods under different compression configurations. When compared to standard HEVC, the BD-rate (PSNR) and BD-rate (SSIM) of the proposed method are up to -19.1% and -26.5%, respectively. | ||
| 650 | 7 | |a Deep learning |2 Elsevier | |
| 650 | 7 | |a High-efficiency video coding (HEVC) |2 Elsevier | |
| 650 | 7 | |a Machine learning |2 Elsevier | |
| 650 | 7 | |a Reference-based super-resolution |2 Elsevier | |
| 650 | 7 | |a Low bitrate |2 Elsevier | |
| 650 | 7 | |a Video compression |2 Elsevier | |
| 700 | 1 | |a Chang, Wenshuai |4 oth | |
| 700 | 1 | |a Quan, Weize |4 oth | |
| 700 | 1 | |a Xiao, Jimin |4 oth | |
| 700 | 1 | |a Yan, Dong-Ming |4 oth | |
| 700 | 1 | |a Gabbouj, Moncef |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier |a Zeng, C. ELSEVIER |t Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis |d 2014 |d theory, techniques & applications |g Amsterdam [u.a.] |w (DE-627)ELV017872103 |
| 773 | 1 | 8 | |g volume:107 |g year:2022 |g pages:0 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.image.2022.116754 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a SSG-OLC-PHA | ||
| 912 | |a GBV_ILN_21 | ||
| 912 | |a GBV_ILN_22 | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_2002 | ||
| 912 | |a GBV_ILN_2005 | ||
| 912 | |a GBV_ILN_2014 | ||
| 912 | |a GBV_ILN_2015 | ||
| 936 | b | k | |a 52.56 |j Regenerative Energieformen |j alternative Energieformen |q VZ |
| 951 | |a AR | ||
| 952 | |d 107 |j 2022 |h 0 | ||
| author_variant |
l y ly |
|---|---|
| matchkey_str |
yulichangwenshuaiquanweizexiaojiminyando:2022----:erletrtaseassevdooigih |
| hierarchy_sort_str |
2022transfer abstract |
| bklnumber |
52.56 |
| publishDate |
2022 |
| allfields |
10.1016/j.image.2022.116754 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001927.pica (DE-627)ELV058357971 (ELSEVIER)S0923-5965(22)00077-7 DE-627 ger DE-627 rakwb eng 610 VZ 660 620 VZ 52.56 bkl Yu, Li verfasserin aut Neural texture transfer assisted video coding with adaptive up-sampling 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Deep learning techniques have been extensively investigated for the purpose of further increasing the efficiency of traditional video compression. Some deep learning techniques for down/up-sampling-based video coding were found to be especially effective when the bandwidth or storage is limited. Existing works mainly differ in the super-resolution models used. Some works simply use a single image super-resolution model, ignoring the rich information in the correlation between video frames, while others explore the correlation between frames by simply concatenating the features across adjacent frames. This, however, may fail when the textures are not well aligned. In this paper, we propose to utilize neural texture transfer which exploits the semantic correlation between frames and is able to explore the correlated information even when the textures are not aligned. Meanwhile, an adaptive group of pictures (GOP) method is proposed to automatically decide whether a frame should be down-sampled or not. Experimental results show that the proposed method outperforms the standard HEVC and state-of-the-art methods under different compression configurations. When compared to standard HEVC, the BD-rate (PSNR) and BD-rate (SSIM) of the proposed method are up to -19.1% and -26.5%, respectively. Deep learning techniques have been extensively investigated for the purpose of further increasing the efficiency of traditional video compression. Some deep learning techniques for down/up-sampling-based video coding were found to be especially effective when the bandwidth or storage is limited. Existing works mainly differ in the super-resolution models used. Some works simply use a single image super-resolution model, ignoring the rich information in the correlation between video frames, while others explore the correlation between frames by simply concatenating the features across adjacent frames. This, however, may fail when the textures are not well aligned. In this paper, we propose to utilize neural texture transfer which exploits the semantic correlation between frames and is able to explore the correlated information even when the textures are not aligned. Meanwhile, an adaptive group of pictures (GOP) method is proposed to automatically decide whether a frame should be down-sampled or not. Experimental results show that the proposed method outperforms the standard HEVC and state-of-the-art methods under different compression configurations. When compared to standard HEVC, the BD-rate (PSNR) and BD-rate (SSIM) of the proposed method are up to -19.1% and -26.5%, respectively. Deep learning Elsevier High-efficiency video coding (HEVC) Elsevier Machine learning Elsevier Reference-based super-resolution Elsevier Low bitrate Elsevier Video compression Elsevier Chang, Wenshuai oth Quan, Weize oth Xiao, Jimin oth Yan, Dong-Ming oth Gabbouj, Moncef oth Enthalten in Elsevier Zeng, C. ELSEVIER Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis 2014 theory, techniques & applications Amsterdam [u.a.] (DE-627)ELV017872103 volume:107 year:2022 pages:0 https://doi.org/10.1016/j.image.2022.116754 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_21 GBV_ILN_22 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2015 52.56 Regenerative Energieformen alternative Energieformen VZ AR 107 2022 0 |
| spelling |
10.1016/j.image.2022.116754 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001927.pica (DE-627)ELV058357971 (ELSEVIER)S0923-5965(22)00077-7 DE-627 ger DE-627 rakwb eng 610 VZ 660 620 VZ 52.56 bkl Yu, Li verfasserin aut Neural texture transfer assisted video coding with adaptive up-sampling 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Deep learning techniques have been extensively investigated for the purpose of further increasing the efficiency of traditional video compression. Some deep learning techniques for down/up-sampling-based video coding were found to be especially effective when the bandwidth or storage is limited. Existing works mainly differ in the super-resolution models used. Some works simply use a single image super-resolution model, ignoring the rich information in the correlation between video frames, while others explore the correlation between frames by simply concatenating the features across adjacent frames. This, however, may fail when the textures are not well aligned. In this paper, we propose to utilize neural texture transfer which exploits the semantic correlation between frames and is able to explore the correlated information even when the textures are not aligned. Meanwhile, an adaptive group of pictures (GOP) method is proposed to automatically decide whether a frame should be down-sampled or not. Experimental results show that the proposed method outperforms the standard HEVC and state-of-the-art methods under different compression configurations. When compared to standard HEVC, the BD-rate (PSNR) and BD-rate (SSIM) of the proposed method are up to -19.1% and -26.5%, respectively. Deep learning techniques have been extensively investigated for the purpose of further increasing the efficiency of traditional video compression. Some deep learning techniques for down/up-sampling-based video coding were found to be especially effective when the bandwidth or storage is limited. Existing works mainly differ in the super-resolution models used. Some works simply use a single image super-resolution model, ignoring the rich information in the correlation between video frames, while others explore the correlation between frames by simply concatenating the features across adjacent frames. This, however, may fail when the textures are not well aligned. In this paper, we propose to utilize neural texture transfer which exploits the semantic correlation between frames and is able to explore the correlated information even when the textures are not aligned. Meanwhile, an adaptive group of pictures (GOP) method is proposed to automatically decide whether a frame should be down-sampled or not. Experimental results show that the proposed method outperforms the standard HEVC and state-of-the-art methods under different compression configurations. When compared to standard HEVC, the BD-rate (PSNR) and BD-rate (SSIM) of the proposed method are up to -19.1% and -26.5%, respectively. Deep learning Elsevier High-efficiency video coding (HEVC) Elsevier Machine learning Elsevier Reference-based super-resolution Elsevier Low bitrate Elsevier Video compression Elsevier Chang, Wenshuai oth Quan, Weize oth Xiao, Jimin oth Yan, Dong-Ming oth Gabbouj, Moncef oth Enthalten in Elsevier Zeng, C. ELSEVIER Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis 2014 theory, techniques & applications Amsterdam [u.a.] (DE-627)ELV017872103 volume:107 year:2022 pages:0 https://doi.org/10.1016/j.image.2022.116754 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_21 GBV_ILN_22 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2015 52.56 Regenerative Energieformen alternative Energieformen VZ AR 107 2022 0 |
| allfields_unstemmed |
10.1016/j.image.2022.116754 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001927.pica (DE-627)ELV058357971 (ELSEVIER)S0923-5965(22)00077-7 DE-627 ger DE-627 rakwb eng 610 VZ 660 620 VZ 52.56 bkl Yu, Li verfasserin aut Neural texture transfer assisted video coding with adaptive up-sampling 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Deep learning techniques have been extensively investigated for the purpose of further increasing the efficiency of traditional video compression. Some deep learning techniques for down/up-sampling-based video coding were found to be especially effective when the bandwidth or storage is limited. Existing works mainly differ in the super-resolution models used. Some works simply use a single image super-resolution model, ignoring the rich information in the correlation between video frames, while others explore the correlation between frames by simply concatenating the features across adjacent frames. This, however, may fail when the textures are not well aligned. In this paper, we propose to utilize neural texture transfer which exploits the semantic correlation between frames and is able to explore the correlated information even when the textures are not aligned. Meanwhile, an adaptive group of pictures (GOP) method is proposed to automatically decide whether a frame should be down-sampled or not. Experimental results show that the proposed method outperforms the standard HEVC and state-of-the-art methods under different compression configurations. When compared to standard HEVC, the BD-rate (PSNR) and BD-rate (SSIM) of the proposed method are up to -19.1% and -26.5%, respectively. Deep learning techniques have been extensively investigated for the purpose of further increasing the efficiency of traditional video compression. Some deep learning techniques for down/up-sampling-based video coding were found to be especially effective when the bandwidth or storage is limited. Existing works mainly differ in the super-resolution models used. Some works simply use a single image super-resolution model, ignoring the rich information in the correlation between video frames, while others explore the correlation between frames by simply concatenating the features across adjacent frames. This, however, may fail when the textures are not well aligned. In this paper, we propose to utilize neural texture transfer which exploits the semantic correlation between frames and is able to explore the correlated information even when the textures are not aligned. Meanwhile, an adaptive group of pictures (GOP) method is proposed to automatically decide whether a frame should be down-sampled or not. Experimental results show that the proposed method outperforms the standard HEVC and state-of-the-art methods under different compression configurations. When compared to standard HEVC, the BD-rate (PSNR) and BD-rate (SSIM) of the proposed method are up to -19.1% and -26.5%, respectively. Deep learning Elsevier High-efficiency video coding (HEVC) Elsevier Machine learning Elsevier Reference-based super-resolution Elsevier Low bitrate Elsevier Video compression Elsevier Chang, Wenshuai oth Quan, Weize oth Xiao, Jimin oth Yan, Dong-Ming oth Gabbouj, Moncef oth Enthalten in Elsevier Zeng, C. ELSEVIER Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis 2014 theory, techniques & applications Amsterdam [u.a.] (DE-627)ELV017872103 volume:107 year:2022 pages:0 https://doi.org/10.1016/j.image.2022.116754 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_21 GBV_ILN_22 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2015 52.56 Regenerative Energieformen alternative Energieformen VZ AR 107 2022 0 |
| allfieldsGer |
10.1016/j.image.2022.116754 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001927.pica (DE-627)ELV058357971 (ELSEVIER)S0923-5965(22)00077-7 DE-627 ger DE-627 rakwb eng 610 VZ 660 620 VZ 52.56 bkl Yu, Li verfasserin aut Neural texture transfer assisted video coding with adaptive up-sampling 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Deep learning techniques have been extensively investigated for the purpose of further increasing the efficiency of traditional video compression. Some deep learning techniques for down/up-sampling-based video coding were found to be especially effective when the bandwidth or storage is limited. Existing works mainly differ in the super-resolution models used. Some works simply use a single image super-resolution model, ignoring the rich information in the correlation between video frames, while others explore the correlation between frames by simply concatenating the features across adjacent frames. This, however, may fail when the textures are not well aligned. In this paper, we propose to utilize neural texture transfer which exploits the semantic correlation between frames and is able to explore the correlated information even when the textures are not aligned. Meanwhile, an adaptive group of pictures (GOP) method is proposed to automatically decide whether a frame should be down-sampled or not. Experimental results show that the proposed method outperforms the standard HEVC and state-of-the-art methods under different compression configurations. When compared to standard HEVC, the BD-rate (PSNR) and BD-rate (SSIM) of the proposed method are up to -19.1% and -26.5%, respectively. Deep learning techniques have been extensively investigated for the purpose of further increasing the efficiency of traditional video compression. Some deep learning techniques for down/up-sampling-based video coding were found to be especially effective when the bandwidth or storage is limited. Existing works mainly differ in the super-resolution models used. Some works simply use a single image super-resolution model, ignoring the rich information in the correlation between video frames, while others explore the correlation between frames by simply concatenating the features across adjacent frames. This, however, may fail when the textures are not well aligned. In this paper, we propose to utilize neural texture transfer which exploits the semantic correlation between frames and is able to explore the correlated information even when the textures are not aligned. Meanwhile, an adaptive group of pictures (GOP) method is proposed to automatically decide whether a frame should be down-sampled or not. Experimental results show that the proposed method outperforms the standard HEVC and state-of-the-art methods under different compression configurations. When compared to standard HEVC, the BD-rate (PSNR) and BD-rate (SSIM) of the proposed method are up to -19.1% and -26.5%, respectively. Deep learning Elsevier High-efficiency video coding (HEVC) Elsevier Machine learning Elsevier Reference-based super-resolution Elsevier Low bitrate Elsevier Video compression Elsevier Chang, Wenshuai oth Quan, Weize oth Xiao, Jimin oth Yan, Dong-Ming oth Gabbouj, Moncef oth Enthalten in Elsevier Zeng, C. ELSEVIER Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis 2014 theory, techniques & applications Amsterdam [u.a.] (DE-627)ELV017872103 volume:107 year:2022 pages:0 https://doi.org/10.1016/j.image.2022.116754 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_21 GBV_ILN_22 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2015 52.56 Regenerative Energieformen alternative Energieformen VZ AR 107 2022 0 |
| allfieldsSound |
10.1016/j.image.2022.116754 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001927.pica (DE-627)ELV058357971 (ELSEVIER)S0923-5965(22)00077-7 DE-627 ger DE-627 rakwb eng 610 VZ 660 620 VZ 52.56 bkl Yu, Li verfasserin aut Neural texture transfer assisted video coding with adaptive up-sampling 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Deep learning techniques have been extensively investigated for the purpose of further increasing the efficiency of traditional video compression. Some deep learning techniques for down/up-sampling-based video coding were found to be especially effective when the bandwidth or storage is limited. Existing works mainly differ in the super-resolution models used. Some works simply use a single image super-resolution model, ignoring the rich information in the correlation between video frames, while others explore the correlation between frames by simply concatenating the features across adjacent frames. This, however, may fail when the textures are not well aligned. In this paper, we propose to utilize neural texture transfer which exploits the semantic correlation between frames and is able to explore the correlated information even when the textures are not aligned. Meanwhile, an adaptive group of pictures (GOP) method is proposed to automatically decide whether a frame should be down-sampled or not. Experimental results show that the proposed method outperforms the standard HEVC and state-of-the-art methods under different compression configurations. When compared to standard HEVC, the BD-rate (PSNR) and BD-rate (SSIM) of the proposed method are up to -19.1% and -26.5%, respectively. Deep learning techniques have been extensively investigated for the purpose of further increasing the efficiency of traditional video compression. Some deep learning techniques for down/up-sampling-based video coding were found to be especially effective when the bandwidth or storage is limited. Existing works mainly differ in the super-resolution models used. Some works simply use a single image super-resolution model, ignoring the rich information in the correlation between video frames, while others explore the correlation between frames by simply concatenating the features across adjacent frames. This, however, may fail when the textures are not well aligned. In this paper, we propose to utilize neural texture transfer which exploits the semantic correlation between frames and is able to explore the correlated information even when the textures are not aligned. Meanwhile, an adaptive group of pictures (GOP) method is proposed to automatically decide whether a frame should be down-sampled or not. Experimental results show that the proposed method outperforms the standard HEVC and state-of-the-art methods under different compression configurations. When compared to standard HEVC, the BD-rate (PSNR) and BD-rate (SSIM) of the proposed method are up to -19.1% and -26.5%, respectively. Deep learning Elsevier High-efficiency video coding (HEVC) Elsevier Machine learning Elsevier Reference-based super-resolution Elsevier Low bitrate Elsevier Video compression Elsevier Chang, Wenshuai oth Quan, Weize oth Xiao, Jimin oth Yan, Dong-Ming oth Gabbouj, Moncef oth Enthalten in Elsevier Zeng, C. ELSEVIER Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis 2014 theory, techniques & applications Amsterdam [u.a.] (DE-627)ELV017872103 volume:107 year:2022 pages:0 https://doi.org/10.1016/j.image.2022.116754 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_21 GBV_ILN_22 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2015 52.56 Regenerative Energieformen alternative Energieformen VZ AR 107 2022 0 |
| language |
English |
| source |
Enthalten in Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis Amsterdam [u.a.] volume:107 year:2022 pages:0 |
| sourceStr |
Enthalten in Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis Amsterdam [u.a.] volume:107 year:2022 pages:0 |
| format_phy_str_mv |
Article |
| bklname |
Regenerative Energieformen alternative Energieformen |
| institution |
findex.gbv.de |
| topic_facet |
Deep learning High-efficiency video coding (HEVC) Machine learning Reference-based super-resolution Low bitrate Video compression |
| dewey-raw |
610 |
| isfreeaccess_bool |
false |
| container_title |
Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis |
| authorswithroles_txt_mv |
Yu, Li @@aut@@ Chang, Wenshuai @@oth@@ Quan, Weize @@oth@@ Xiao, Jimin @@oth@@ Yan, Dong-Ming @@oth@@ Gabbouj, Moncef @@oth@@ |
| publishDateDaySort_date |
2022-01-01T00:00:00Z |
| hierarchy_top_id |
ELV017872103 |
| dewey-sort |
3610 |
| id |
ELV058357971 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV058357971</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626050723.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220808s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.image.2022.116754</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001927.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV058357971</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0923-5965(22)00077-7</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">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">660</subfield><subfield code="a">620</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">52.56</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Yu, Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Neural texture transfer assisted video coding with adaptive up-sampling</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Deep learning techniques have been extensively investigated for the purpose of further increasing the efficiency of traditional video compression. Some deep learning techniques for down/up-sampling-based video coding were found to be especially effective when the bandwidth or storage is limited. Existing works mainly differ in the super-resolution models used. Some works simply use a single image super-resolution model, ignoring the rich information in the correlation between video frames, while others explore the correlation between frames by simply concatenating the features across adjacent frames. This, however, may fail when the textures are not well aligned. In this paper, we propose to utilize neural texture transfer which exploits the semantic correlation between frames and is able to explore the correlated information even when the textures are not aligned. Meanwhile, an adaptive group of pictures (GOP) method is proposed to automatically decide whether a frame should be down-sampled or not. Experimental results show that the proposed method outperforms the standard HEVC and state-of-the-art methods under different compression configurations. When compared to standard HEVC, the BD-rate (PSNR) and BD-rate (SSIM) of the proposed method are up to -19.1% and -26.5%, respectively.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Deep learning techniques have been extensively investigated for the purpose of further increasing the efficiency of traditional video compression. Some deep learning techniques for down/up-sampling-based video coding were found to be especially effective when the bandwidth or storage is limited. Existing works mainly differ in the super-resolution models used. Some works simply use a single image super-resolution model, ignoring the rich information in the correlation between video frames, while others explore the correlation between frames by simply concatenating the features across adjacent frames. This, however, may fail when the textures are not well aligned. In this paper, we propose to utilize neural texture transfer which exploits the semantic correlation between frames and is able to explore the correlated information even when the textures are not aligned. Meanwhile, an adaptive group of pictures (GOP) method is proposed to automatically decide whether a frame should be down-sampled or not. Experimental results show that the proposed method outperforms the standard HEVC and state-of-the-art methods under different compression configurations. When compared to standard HEVC, the BD-rate (PSNR) and BD-rate (SSIM) of the proposed method are up to -19.1% and -26.5%, respectively.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Deep learning</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">High-efficiency video coding (HEVC)</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Machine learning</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Reference-based super-resolution</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Low bitrate</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Video compression</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chang, Wenshuai</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Quan, Weize</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xiao, Jimin</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yan, Dong-Ming</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gabbouj, Moncef</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Zeng, C. ELSEVIER</subfield><subfield code="t">Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis</subfield><subfield code="d">2014</subfield><subfield code="d">theory, techniques & applications</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV017872103</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:107</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.image.2022.116754</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_21</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2002</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">52.56</subfield><subfield code="j">Regenerative Energieformen</subfield><subfield code="j">alternative Energieformen</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">107</subfield><subfield code="j">2022</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| author |
Yu, Li |
| spellingShingle |
Yu, Li ddc 610 ddc 660 bkl 52.56 Elsevier Deep learning Elsevier High-efficiency video coding (HEVC) Elsevier Machine learning Elsevier Reference-based super-resolution Elsevier Low bitrate Elsevier Video compression Neural texture transfer assisted video coding with adaptive up-sampling |
| authorStr |
Yu, Li |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV017872103 |
| format |
electronic Article |
| dewey-ones |
610 - Medicine & health 660 - Chemical engineering 620 - Engineering & allied operations |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
610 VZ 660 620 VZ 52.56 bkl Neural texture transfer assisted video coding with adaptive up-sampling Deep learning Elsevier High-efficiency video coding (HEVC) Elsevier Machine learning Elsevier Reference-based super-resolution Elsevier Low bitrate Elsevier Video compression Elsevier |
| topic |
ddc 610 ddc 660 bkl 52.56 Elsevier Deep learning Elsevier High-efficiency video coding (HEVC) Elsevier Machine learning Elsevier Reference-based super-resolution Elsevier Low bitrate Elsevier Video compression |
| topic_unstemmed |
ddc 610 ddc 660 bkl 52.56 Elsevier Deep learning Elsevier High-efficiency video coding (HEVC) Elsevier Machine learning Elsevier Reference-based super-resolution Elsevier Low bitrate Elsevier Video compression |
| topic_browse |
ddc 610 ddc 660 bkl 52.56 Elsevier Deep learning Elsevier High-efficiency video coding (HEVC) Elsevier Machine learning Elsevier Reference-based super-resolution Elsevier Low bitrate Elsevier Video compression |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
w c wc w q wq j x jx d m y dmy m g mg |
| hierarchy_parent_title |
Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis |
| hierarchy_parent_id |
ELV017872103 |
| dewey-tens |
610 - Medicine & health 660 - Chemical engineering 620 - Engineering |
| hierarchy_top_title |
Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV017872103 |
| title |
Neural texture transfer assisted video coding with adaptive up-sampling |
| ctrlnum |
(DE-627)ELV058357971 (ELSEVIER)S0923-5965(22)00077-7 |
| title_full |
Neural texture transfer assisted video coding with adaptive up-sampling |
| author_sort |
Yu, Li |
| journal |
Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis |
| journalStr |
Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology |
| recordtype |
marc |
| publishDateSort |
2022 |
| contenttype_str_mv |
zzz |
| container_start_page |
0 |
| author_browse |
Yu, Li |
| container_volume |
107 |
| class |
610 VZ 660 620 VZ 52.56 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Yu, Li |
| doi_str_mv |
10.1016/j.image.2022.116754 |
| dewey-full |
610 660 620 |
| title_sort |
neural texture transfer assisted video coding with adaptive up-sampling |
| title_auth |
Neural texture transfer assisted video coding with adaptive up-sampling |
| abstract |
Deep learning techniques have been extensively investigated for the purpose of further increasing the efficiency of traditional video compression. Some deep learning techniques for down/up-sampling-based video coding were found to be especially effective when the bandwidth or storage is limited. Existing works mainly differ in the super-resolution models used. Some works simply use a single image super-resolution model, ignoring the rich information in the correlation between video frames, while others explore the correlation between frames by simply concatenating the features across adjacent frames. This, however, may fail when the textures are not well aligned. In this paper, we propose to utilize neural texture transfer which exploits the semantic correlation between frames and is able to explore the correlated information even when the textures are not aligned. Meanwhile, an adaptive group of pictures (GOP) method is proposed to automatically decide whether a frame should be down-sampled or not. Experimental results show that the proposed method outperforms the standard HEVC and state-of-the-art methods under different compression configurations. When compared to standard HEVC, the BD-rate (PSNR) and BD-rate (SSIM) of the proposed method are up to -19.1% and -26.5%, respectively. |
| abstractGer |
Deep learning techniques have been extensively investigated for the purpose of further increasing the efficiency of traditional video compression. Some deep learning techniques for down/up-sampling-based video coding were found to be especially effective when the bandwidth or storage is limited. Existing works mainly differ in the super-resolution models used. Some works simply use a single image super-resolution model, ignoring the rich information in the correlation between video frames, while others explore the correlation between frames by simply concatenating the features across adjacent frames. This, however, may fail when the textures are not well aligned. In this paper, we propose to utilize neural texture transfer which exploits the semantic correlation between frames and is able to explore the correlated information even when the textures are not aligned. Meanwhile, an adaptive group of pictures (GOP) method is proposed to automatically decide whether a frame should be down-sampled or not. Experimental results show that the proposed method outperforms the standard HEVC and state-of-the-art methods under different compression configurations. When compared to standard HEVC, the BD-rate (PSNR) and BD-rate (SSIM) of the proposed method are up to -19.1% and -26.5%, respectively. |
| abstract_unstemmed |
Deep learning techniques have been extensively investigated for the purpose of further increasing the efficiency of traditional video compression. Some deep learning techniques for down/up-sampling-based video coding were found to be especially effective when the bandwidth or storage is limited. Existing works mainly differ in the super-resolution models used. Some works simply use a single image super-resolution model, ignoring the rich information in the correlation between video frames, while others explore the correlation between frames by simply concatenating the features across adjacent frames. This, however, may fail when the textures are not well aligned. In this paper, we propose to utilize neural texture transfer which exploits the semantic correlation between frames and is able to explore the correlated information even when the textures are not aligned. Meanwhile, an adaptive group of pictures (GOP) method is proposed to automatically decide whether a frame should be down-sampled or not. Experimental results show that the proposed method outperforms the standard HEVC and state-of-the-art methods under different compression configurations. When compared to standard HEVC, the BD-rate (PSNR) and BD-rate (SSIM) of the proposed method are up to -19.1% and -26.5%, respectively. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_21 GBV_ILN_22 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2015 |
| title_short |
Neural texture transfer assisted video coding with adaptive up-sampling |
| url |
https://doi.org/10.1016/j.image.2022.116754 |
| remote_bool |
true |
| author2 |
Chang, Wenshuai Quan, Weize Xiao, Jimin Yan, Dong-Ming Gabbouj, Moncef |
| author2Str |
Chang, Wenshuai Quan, Weize Xiao, Jimin Yan, Dong-Ming Gabbouj, Moncef |
| ppnlink |
ELV017872103 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth oth oth |
| doi_str |
10.1016/j.image.2022.116754 |
| up_date |
2024-07-06T18:47:16.581Z |
| _version_ |
1803856523120607232 |
| 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">ELV058357971</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626050723.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220808s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.image.2022.116754</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001927.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV058357971</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0923-5965(22)00077-7</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">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">660</subfield><subfield code="a">620</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">52.56</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Yu, Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Neural texture transfer assisted video coding with adaptive up-sampling</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Deep learning techniques have been extensively investigated for the purpose of further increasing the efficiency of traditional video compression. Some deep learning techniques for down/up-sampling-based video coding were found to be especially effective when the bandwidth or storage is limited. Existing works mainly differ in the super-resolution models used. Some works simply use a single image super-resolution model, ignoring the rich information in the correlation between video frames, while others explore the correlation between frames by simply concatenating the features across adjacent frames. This, however, may fail when the textures are not well aligned. In this paper, we propose to utilize neural texture transfer which exploits the semantic correlation between frames and is able to explore the correlated information even when the textures are not aligned. Meanwhile, an adaptive group of pictures (GOP) method is proposed to automatically decide whether a frame should be down-sampled or not. Experimental results show that the proposed method outperforms the standard HEVC and state-of-the-art methods under different compression configurations. When compared to standard HEVC, the BD-rate (PSNR) and BD-rate (SSIM) of the proposed method are up to -19.1% and -26.5%, respectively.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Deep learning techniques have been extensively investigated for the purpose of further increasing the efficiency of traditional video compression. Some deep learning techniques for down/up-sampling-based video coding were found to be especially effective when the bandwidth or storage is limited. Existing works mainly differ in the super-resolution models used. Some works simply use a single image super-resolution model, ignoring the rich information in the correlation between video frames, while others explore the correlation between frames by simply concatenating the features across adjacent frames. This, however, may fail when the textures are not well aligned. In this paper, we propose to utilize neural texture transfer which exploits the semantic correlation between frames and is able to explore the correlated information even when the textures are not aligned. Meanwhile, an adaptive group of pictures (GOP) method is proposed to automatically decide whether a frame should be down-sampled or not. Experimental results show that the proposed method outperforms the standard HEVC and state-of-the-art methods under different compression configurations. When compared to standard HEVC, the BD-rate (PSNR) and BD-rate (SSIM) of the proposed method are up to -19.1% and -26.5%, respectively.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Deep learning</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">High-efficiency video coding (HEVC)</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Machine learning</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Reference-based super-resolution</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Low bitrate</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Video compression</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chang, Wenshuai</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Quan, Weize</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xiao, Jimin</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yan, Dong-Ming</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gabbouj, Moncef</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Zeng, C. ELSEVIER</subfield><subfield code="t">Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis</subfield><subfield code="d">2014</subfield><subfield code="d">theory, techniques & applications</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV017872103</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:107</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.image.2022.116754</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_21</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2002</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">52.56</subfield><subfield code="j">Regenerative Energieformen</subfield><subfield code="j">alternative Energieformen</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">107</subfield><subfield code="j">2022</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| score |
7.4011927 |