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
Autor*in: |
Yu, Li [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2022transfer abstract |
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Schlagwörter: |
High-efficiency video coding (HEVC) |
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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.] |
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Übergeordnetes Werk: |
volume:107 ; year:2022 ; pages:0 |
Links: |
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DOI / URN: |
10.1016/j.image.2022.116754 |
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ELV058357971 |
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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 | |
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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 | |
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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 |
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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 |
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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 |
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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 |
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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 |
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Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis |
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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. |
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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. |
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Neural texture transfer assisted video coding with adaptive up-sampling |
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