Video analytical coding: When video coding meets video analysis
Leveraging on the properties of human visual system, most of the well-designed video coding standards utilize rate–distortion optimization techniques by maximizing a fidelity cost function (e.g., peak signal noise ratio, PSNR) under an available bit rate budget constrain. However, a huge amount of v...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Liu, Yuyang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018transfer abstract |
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| Schlagwörter: |
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| Umfang: |
10 |
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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:67 ; year:2018 ; pages:48-57 ; extent:10 |
| Links: |
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| DOI / URN: |
10.1016/j.image.2018.05.012 |
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ELV04471503X |
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| 520 | |a Leveraging on the properties of human visual system, most of the well-designed video coding standards utilize rate–distortion optimization techniques by maximizing a fidelity cost function (e.g., peak signal noise ratio, PSNR) under an available bit rate budget constrain. However, a huge amount of video data is consumed by computers rather than by human beings in several application scenarios. In view of this, this paper proposes a new coding framework called video analytical coding (VAC) for video analysis. We use the term “analytical distortion” to denote the difference of video analysis performance when video quality degrades and analytical distortion is estimated by compression distortion. Meanwhile, we develop a new rate–analytical-distortion optimization (RADO) method to trade off the bit rate and the analytical distortion. Specifically, we consider moving object detection as the analysis task and develop a rate analytical distortion (RAD) model and a quantization parameter adaptation strategy for video coding, where the analytical distortion is related to the object detection performance represented as F1-measure. Experimental results show that the performance of the video analysis task can be significantly improved (up to 40% reduction of analytical distortion). | ||
| 520 | |a Leveraging on the properties of human visual system, most of the well-designed video coding standards utilize rate–distortion optimization techniques by maximizing a fidelity cost function (e.g., peak signal noise ratio, PSNR) under an available bit rate budget constrain. However, a huge amount of video data is consumed by computers rather than by human beings in several application scenarios. In view of this, this paper proposes a new coding framework called video analytical coding (VAC) for video analysis. We use the term “analytical distortion” to denote the difference of video analysis performance when video quality degrades and analytical distortion is estimated by compression distortion. Meanwhile, we develop a new rate–analytical-distortion optimization (RADO) method to trade off the bit rate and the analytical distortion. Specifically, we consider moving object detection as the analysis task and develop a rate analytical distortion (RAD) model and a quantization parameter adaptation strategy for video coding, where the analytical distortion is related to the object detection performance represented as F1-measure. Experimental results show that the performance of the video analysis task can be significantly improved (up to 40% reduction of analytical distortion). | ||
| 650 | 7 | |a Video analysis |2 Elsevier | |
| 650 | 7 | |a Video analytical coding |2 Elsevier | |
| 650 | 7 | |a Analytical distortion |2 Elsevier | |
| 650 | 7 | |a Rate distortion optimization |2 Elsevier | |
| 700 | 1 | |a Zhu, Ce |4 oth | |
| 700 | 1 | |a Mao, Min |4 oth | |
| 700 | 1 | |a Song, Fangliang |4 oth | |
| 700 | 1 | |a Dufaux, Frederic |4 oth | |
| 700 | 1 | |a Zhang, Xiang |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 |
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10.1016/j.image.2018.05.012 doi GBV00000000000309_01.pica (DE-627)ELV04471503X (ELSEVIER)S0923-5965(18)30498-3 DE-627 ger DE-627 rakwb eng 610 VZ 660 620 VZ 52.56 bkl Liu, Yuyang verfasserin aut Video analytical coding: When video coding meets video analysis 2018transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Leveraging on the properties of human visual system, most of the well-designed video coding standards utilize rate–distortion optimization techniques by maximizing a fidelity cost function (e.g., peak signal noise ratio, PSNR) under an available bit rate budget constrain. However, a huge amount of video data is consumed by computers rather than by human beings in several application scenarios. In view of this, this paper proposes a new coding framework called video analytical coding (VAC) for video analysis. We use the term “analytical distortion” to denote the difference of video analysis performance when video quality degrades and analytical distortion is estimated by compression distortion. Meanwhile, we develop a new rate–analytical-distortion optimization (RADO) method to trade off the bit rate and the analytical distortion. Specifically, we consider moving object detection as the analysis task and develop a rate analytical distortion (RAD) model and a quantization parameter adaptation strategy for video coding, where the analytical distortion is related to the object detection performance represented as F1-measure. Experimental results show that the performance of the video analysis task can be significantly improved (up to 40% reduction of analytical distortion). Leveraging on the properties of human visual system, most of the well-designed video coding standards utilize rate–distortion optimization techniques by maximizing a fidelity cost function (e.g., peak signal noise ratio, PSNR) under an available bit rate budget constrain. However, a huge amount of video data is consumed by computers rather than by human beings in several application scenarios. In view of this, this paper proposes a new coding framework called video analytical coding (VAC) for video analysis. We use the term “analytical distortion” to denote the difference of video analysis performance when video quality degrades and analytical distortion is estimated by compression distortion. Meanwhile, we develop a new rate–analytical-distortion optimization (RADO) method to trade off the bit rate and the analytical distortion. Specifically, we consider moving object detection as the analysis task and develop a rate analytical distortion (RAD) model and a quantization parameter adaptation strategy for video coding, where the analytical distortion is related to the object detection performance represented as F1-measure. Experimental results show that the performance of the video analysis task can be significantly improved (up to 40% reduction of analytical distortion). Video analysis Elsevier Video analytical coding Elsevier Analytical distortion Elsevier Rate distortion optimization Elsevier Zhu, Ce oth Mao, Min oth Song, Fangliang oth Dufaux, Frederic oth Zhang, Xiang 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:67 year:2018 pages:48-57 extent:10 https://doi.org/10.1016/j.image.2018.05.012 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 67 2018 48-57 10 |
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10.1016/j.image.2018.05.012 doi GBV00000000000309_01.pica (DE-627)ELV04471503X (ELSEVIER)S0923-5965(18)30498-3 DE-627 ger DE-627 rakwb eng 610 VZ 660 620 VZ 52.56 bkl Liu, Yuyang verfasserin aut Video analytical coding: When video coding meets video analysis 2018transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Leveraging on the properties of human visual system, most of the well-designed video coding standards utilize rate–distortion optimization techniques by maximizing a fidelity cost function (e.g., peak signal noise ratio, PSNR) under an available bit rate budget constrain. However, a huge amount of video data is consumed by computers rather than by human beings in several application scenarios. In view of this, this paper proposes a new coding framework called video analytical coding (VAC) for video analysis. We use the term “analytical distortion” to denote the difference of video analysis performance when video quality degrades and analytical distortion is estimated by compression distortion. Meanwhile, we develop a new rate–analytical-distortion optimization (RADO) method to trade off the bit rate and the analytical distortion. Specifically, we consider moving object detection as the analysis task and develop a rate analytical distortion (RAD) model and a quantization parameter adaptation strategy for video coding, where the analytical distortion is related to the object detection performance represented as F1-measure. Experimental results show that the performance of the video analysis task can be significantly improved (up to 40% reduction of analytical distortion). Leveraging on the properties of human visual system, most of the well-designed video coding standards utilize rate–distortion optimization techniques by maximizing a fidelity cost function (e.g., peak signal noise ratio, PSNR) under an available bit rate budget constrain. However, a huge amount of video data is consumed by computers rather than by human beings in several application scenarios. In view of this, this paper proposes a new coding framework called video analytical coding (VAC) for video analysis. We use the term “analytical distortion” to denote the difference of video analysis performance when video quality degrades and analytical distortion is estimated by compression distortion. Meanwhile, we develop a new rate–analytical-distortion optimization (RADO) method to trade off the bit rate and the analytical distortion. Specifically, we consider moving object detection as the analysis task and develop a rate analytical distortion (RAD) model and a quantization parameter adaptation strategy for video coding, where the analytical distortion is related to the object detection performance represented as F1-measure. Experimental results show that the performance of the video analysis task can be significantly improved (up to 40% reduction of analytical distortion). Video analysis Elsevier Video analytical coding Elsevier Analytical distortion Elsevier Rate distortion optimization Elsevier Zhu, Ce oth Mao, Min oth Song, Fangliang oth Dufaux, Frederic oth Zhang, Xiang 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:67 year:2018 pages:48-57 extent:10 https://doi.org/10.1016/j.image.2018.05.012 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 67 2018 48-57 10 |
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10.1016/j.image.2018.05.012 doi GBV00000000000309_01.pica (DE-627)ELV04471503X (ELSEVIER)S0923-5965(18)30498-3 DE-627 ger DE-627 rakwb eng 610 VZ 660 620 VZ 52.56 bkl Liu, Yuyang verfasserin aut Video analytical coding: When video coding meets video analysis 2018transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Leveraging on the properties of human visual system, most of the well-designed video coding standards utilize rate–distortion optimization techniques by maximizing a fidelity cost function (e.g., peak signal noise ratio, PSNR) under an available bit rate budget constrain. However, a huge amount of video data is consumed by computers rather than by human beings in several application scenarios. In view of this, this paper proposes a new coding framework called video analytical coding (VAC) for video analysis. We use the term “analytical distortion” to denote the difference of video analysis performance when video quality degrades and analytical distortion is estimated by compression distortion. Meanwhile, we develop a new rate–analytical-distortion optimization (RADO) method to trade off the bit rate and the analytical distortion. Specifically, we consider moving object detection as the analysis task and develop a rate analytical distortion (RAD) model and a quantization parameter adaptation strategy for video coding, where the analytical distortion is related to the object detection performance represented as F1-measure. Experimental results show that the performance of the video analysis task can be significantly improved (up to 40% reduction of analytical distortion). Leveraging on the properties of human visual system, most of the well-designed video coding standards utilize rate–distortion optimization techniques by maximizing a fidelity cost function (e.g., peak signal noise ratio, PSNR) under an available bit rate budget constrain. However, a huge amount of video data is consumed by computers rather than by human beings in several application scenarios. In view of this, this paper proposes a new coding framework called video analytical coding (VAC) for video analysis. We use the term “analytical distortion” to denote the difference of video analysis performance when video quality degrades and analytical distortion is estimated by compression distortion. Meanwhile, we develop a new rate–analytical-distortion optimization (RADO) method to trade off the bit rate and the analytical distortion. Specifically, we consider moving object detection as the analysis task and develop a rate analytical distortion (RAD) model and a quantization parameter adaptation strategy for video coding, where the analytical distortion is related to the object detection performance represented as F1-measure. Experimental results show that the performance of the video analysis task can be significantly improved (up to 40% reduction of analytical distortion). Video analysis Elsevier Video analytical coding Elsevier Analytical distortion Elsevier Rate distortion optimization Elsevier Zhu, Ce oth Mao, Min oth Song, Fangliang oth Dufaux, Frederic oth Zhang, Xiang 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:67 year:2018 pages:48-57 extent:10 https://doi.org/10.1016/j.image.2018.05.012 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 67 2018 48-57 10 |
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10.1016/j.image.2018.05.012 doi GBV00000000000309_01.pica (DE-627)ELV04471503X (ELSEVIER)S0923-5965(18)30498-3 DE-627 ger DE-627 rakwb eng 610 VZ 660 620 VZ 52.56 bkl Liu, Yuyang verfasserin aut Video analytical coding: When video coding meets video analysis 2018transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Leveraging on the properties of human visual system, most of the well-designed video coding standards utilize rate–distortion optimization techniques by maximizing a fidelity cost function (e.g., peak signal noise ratio, PSNR) under an available bit rate budget constrain. However, a huge amount of video data is consumed by computers rather than by human beings in several application scenarios. In view of this, this paper proposes a new coding framework called video analytical coding (VAC) for video analysis. We use the term “analytical distortion” to denote the difference of video analysis performance when video quality degrades and analytical distortion is estimated by compression distortion. Meanwhile, we develop a new rate–analytical-distortion optimization (RADO) method to trade off the bit rate and the analytical distortion. Specifically, we consider moving object detection as the analysis task and develop a rate analytical distortion (RAD) model and a quantization parameter adaptation strategy for video coding, where the analytical distortion is related to the object detection performance represented as F1-measure. Experimental results show that the performance of the video analysis task can be significantly improved (up to 40% reduction of analytical distortion). Leveraging on the properties of human visual system, most of the well-designed video coding standards utilize rate–distortion optimization techniques by maximizing a fidelity cost function (e.g., peak signal noise ratio, PSNR) under an available bit rate budget constrain. However, a huge amount of video data is consumed by computers rather than by human beings in several application scenarios. In view of this, this paper proposes a new coding framework called video analytical coding (VAC) for video analysis. We use the term “analytical distortion” to denote the difference of video analysis performance when video quality degrades and analytical distortion is estimated by compression distortion. Meanwhile, we develop a new rate–analytical-distortion optimization (RADO) method to trade off the bit rate and the analytical distortion. Specifically, we consider moving object detection as the analysis task and develop a rate analytical distortion (RAD) model and a quantization parameter adaptation strategy for video coding, where the analytical distortion is related to the object detection performance represented as F1-measure. Experimental results show that the performance of the video analysis task can be significantly improved (up to 40% reduction of analytical distortion). Video analysis Elsevier Video analytical coding Elsevier Analytical distortion Elsevier Rate distortion optimization Elsevier Zhu, Ce oth Mao, Min oth Song, Fangliang oth Dufaux, Frederic oth Zhang, Xiang 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:67 year:2018 pages:48-57 extent:10 https://doi.org/10.1016/j.image.2018.05.012 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 67 2018 48-57 10 |
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10.1016/j.image.2018.05.012 doi GBV00000000000309_01.pica (DE-627)ELV04471503X (ELSEVIER)S0923-5965(18)30498-3 DE-627 ger DE-627 rakwb eng 610 VZ 660 620 VZ 52.56 bkl Liu, Yuyang verfasserin aut Video analytical coding: When video coding meets video analysis 2018transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Leveraging on the properties of human visual system, most of the well-designed video coding standards utilize rate–distortion optimization techniques by maximizing a fidelity cost function (e.g., peak signal noise ratio, PSNR) under an available bit rate budget constrain. However, a huge amount of video data is consumed by computers rather than by human beings in several application scenarios. In view of this, this paper proposes a new coding framework called video analytical coding (VAC) for video analysis. We use the term “analytical distortion” to denote the difference of video analysis performance when video quality degrades and analytical distortion is estimated by compression distortion. Meanwhile, we develop a new rate–analytical-distortion optimization (RADO) method to trade off the bit rate and the analytical distortion. Specifically, we consider moving object detection as the analysis task and develop a rate analytical distortion (RAD) model and a quantization parameter adaptation strategy for video coding, where the analytical distortion is related to the object detection performance represented as F1-measure. Experimental results show that the performance of the video analysis task can be significantly improved (up to 40% reduction of analytical distortion). Leveraging on the properties of human visual system, most of the well-designed video coding standards utilize rate–distortion optimization techniques by maximizing a fidelity cost function (e.g., peak signal noise ratio, PSNR) under an available bit rate budget constrain. However, a huge amount of video data is consumed by computers rather than by human beings in several application scenarios. In view of this, this paper proposes a new coding framework called video analytical coding (VAC) for video analysis. We use the term “analytical distortion” to denote the difference of video analysis performance when video quality degrades and analytical distortion is estimated by compression distortion. Meanwhile, we develop a new rate–analytical-distortion optimization (RADO) method to trade off the bit rate and the analytical distortion. Specifically, we consider moving object detection as the analysis task and develop a rate analytical distortion (RAD) model and a quantization parameter adaptation strategy for video coding, where the analytical distortion is related to the object detection performance represented as F1-measure. Experimental results show that the performance of the video analysis task can be significantly improved (up to 40% reduction of analytical distortion). Video analysis Elsevier Video analytical coding Elsevier Analytical distortion Elsevier Rate distortion optimization Elsevier Zhu, Ce oth Mao, Min oth Song, Fangliang oth Dufaux, Frederic oth Zhang, Xiang 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:67 year:2018 pages:48-57 extent:10 https://doi.org/10.1016/j.image.2018.05.012 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 67 2018 48-57 10 |
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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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Leveraging on the properties of human visual system, most of the well-designed video coding standards utilize rate–distortion optimization techniques by maximizing a fidelity cost function (e.g., peak signal noise ratio, PSNR) under an available bit rate budget constrain. However, a huge amount of video data is consumed by computers rather than by human beings in several application scenarios. In view of this, this paper proposes a new coding framework called video analytical coding (VAC) for video analysis. We use the term “analytical distortion” to denote the difference of video analysis performance when video quality degrades and analytical distortion is estimated by compression distortion. Meanwhile, we develop a new rate–analytical-distortion optimization (RADO) method to trade off the bit rate and the analytical distortion. Specifically, we consider moving object detection as the analysis task and develop a rate analytical distortion (RAD) model and a quantization parameter adaptation strategy for video coding, where the analytical distortion is related to the object detection performance represented as F1-measure. Experimental results show that the performance of the video analysis task can be significantly improved (up to 40% reduction of analytical distortion). |
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Leveraging on the properties of human visual system, most of the well-designed video coding standards utilize rate–distortion optimization techniques by maximizing a fidelity cost function (e.g., peak signal noise ratio, PSNR) under an available bit rate budget constrain. However, a huge amount of video data is consumed by computers rather than by human beings in several application scenarios. In view of this, this paper proposes a new coding framework called video analytical coding (VAC) for video analysis. We use the term “analytical distortion” to denote the difference of video analysis performance when video quality degrades and analytical distortion is estimated by compression distortion. Meanwhile, we develop a new rate–analytical-distortion optimization (RADO) method to trade off the bit rate and the analytical distortion. Specifically, we consider moving object detection as the analysis task and develop a rate analytical distortion (RAD) model and a quantization parameter adaptation strategy for video coding, where the analytical distortion is related to the object detection performance represented as F1-measure. Experimental results show that the performance of the video analysis task can be significantly improved (up to 40% reduction of analytical distortion). |
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Leveraging on the properties of human visual system, most of the well-designed video coding standards utilize rate–distortion optimization techniques by maximizing a fidelity cost function (e.g., peak signal noise ratio, PSNR) under an available bit rate budget constrain. However, a huge amount of video data is consumed by computers rather than by human beings in several application scenarios. In view of this, this paper proposes a new coding framework called video analytical coding (VAC) for video analysis. We use the term “analytical distortion” to denote the difference of video analysis performance when video quality degrades and analytical distortion is estimated by compression distortion. Meanwhile, we develop a new rate–analytical-distortion optimization (RADO) method to trade off the bit rate and the analytical distortion. Specifically, we consider moving object detection as the analysis task and develop a rate analytical distortion (RAD) model and a quantization parameter adaptation strategy for video coding, where the analytical distortion is related to the object detection performance represented as F1-measure. Experimental results show that the performance of the video analysis task can be significantly improved (up to 40% reduction of analytical distortion). |
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