Quality Assessment for DIBR-Synthesized Images With Local and Global Distortions

Depth-Image-Based-Rendering (DIBR), as one important technique in 3D video system, can be used to generate virtual views. Unfortunately, the DIBR algorithms will introduce various distortions and induce an annoying viewing experience. And it has been proved that traditional 2D assessment quality met...
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Autor*in:	Laihua Wang [verfasserIn] Yue Zhao [verfasserIn] Xu Ma [verfasserIn] Sumin Qi [verfasserIn] Weiqing Yan [verfasserIn] Hua Chen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	DIBR synthesis distortions quality evaluation view synthesis perceptual quality

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 8(2020), Seite 27938-27948
Übergeordnetes Werk:	volume:8 ; year:2020 ; pages:27938-27948

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2020.2971995

Katalog-ID:	DOAJ058896546

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spelling	10.1109/ACCESS.2020.2971995 doi (DE-627)DOAJ058896546 (DE-599)DOAJcd1767ee0df34d51b30ed02843ab7c8f DE-627 ger DE-627 rakwb eng TK1-9971 Laihua Wang verfasserin aut Quality Assessment for DIBR-Synthesized Images With Local and Global Distortions 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Depth-Image-Based-Rendering (DIBR), as one important technique in 3D video system, can be used to generate virtual views. Unfortunately, the DIBR algorithms will introduce various distortions and induce an annoying viewing experience. And it has been proved that traditional 2D assessment quality metrics are not suitable for the DIBR-synthesized views. In this paper, we propose a novel approach to assess the quality of DIBR-synthesized images. The proposed method mainly considers three kinds of DIBR-related distortions: holes distortion, strip-sharped distortion and global sharpness. Holes and strip distortions as two local features are used to characterize the local quality of DIBR-synthesized image, respectively. For the global sharpness we consider the Just Notice Difference (JND) model of human eyes and use it to extract the JND-based global difference for analyzing the global quality. Finally, we combine the holes distortion evaluation, strip distortion evaluation and global quality to infer the overall perceptual quality. Extensive experiments indicate that our method achieves higher accuracy of quality prediction than most competing metrics. DIBR synthesis distortions quality evaluation view synthesis perceptual quality Electrical engineering. Electronics. Nuclear engineering Yue Zhao verfasserin aut Xu Ma verfasserin aut Sumin Qi verfasserin aut Weiqing Yan verfasserin aut Hua Chen verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 27938-27948 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:27938-27948 https://doi.org/10.1109/ACCESS.2020.2971995 kostenfrei https://doaj.org/article/cd1767ee0df34d51b30ed02843ab7c8f kostenfrei https://ieeexplore.ieee.org/document/8985347/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 27938-27948
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allfieldsGer	10.1109/ACCESS.2020.2971995 doi (DE-627)DOAJ058896546 (DE-599)DOAJcd1767ee0df34d51b30ed02843ab7c8f DE-627 ger DE-627 rakwb eng TK1-9971 Laihua Wang verfasserin aut Quality Assessment for DIBR-Synthesized Images With Local and Global Distortions 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Depth-Image-Based-Rendering (DIBR), as one important technique in 3D video system, can be used to generate virtual views. Unfortunately, the DIBR algorithms will introduce various distortions and induce an annoying viewing experience. And it has been proved that traditional 2D assessment quality metrics are not suitable for the DIBR-synthesized views. In this paper, we propose a novel approach to assess the quality of DIBR-synthesized images. The proposed method mainly considers three kinds of DIBR-related distortions: holes distortion, strip-sharped distortion and global sharpness. Holes and strip distortions as two local features are used to characterize the local quality of DIBR-synthesized image, respectively. For the global sharpness we consider the Just Notice Difference (JND) model of human eyes and use it to extract the JND-based global difference for analyzing the global quality. Finally, we combine the holes distortion evaluation, strip distortion evaluation and global quality to infer the overall perceptual quality. Extensive experiments indicate that our method achieves higher accuracy of quality prediction than most competing metrics. DIBR synthesis distortions quality evaluation view synthesis perceptual quality Electrical engineering. Electronics. Nuclear engineering Yue Zhao verfasserin aut Xu Ma verfasserin aut Sumin Qi verfasserin aut Weiqing Yan verfasserin aut Hua Chen verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 27938-27948 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:27938-27948 https://doi.org/10.1109/ACCESS.2020.2971995 kostenfrei https://doaj.org/article/cd1767ee0df34d51b30ed02843ab7c8f kostenfrei https://ieeexplore.ieee.org/document/8985347/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 27938-27948
allfieldsSound	10.1109/ACCESS.2020.2971995 doi (DE-627)DOAJ058896546 (DE-599)DOAJcd1767ee0df34d51b30ed02843ab7c8f DE-627 ger DE-627 rakwb eng TK1-9971 Laihua Wang verfasserin aut Quality Assessment for DIBR-Synthesized Images With Local and Global Distortions 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Depth-Image-Based-Rendering (DIBR), as one important technique in 3D video system, can be used to generate virtual views. Unfortunately, the DIBR algorithms will introduce various distortions and induce an annoying viewing experience. And it has been proved that traditional 2D assessment quality metrics are not suitable for the DIBR-synthesized views. In this paper, we propose a novel approach to assess the quality of DIBR-synthesized images. The proposed method mainly considers three kinds of DIBR-related distortions: holes distortion, strip-sharped distortion and global sharpness. Holes and strip distortions as two local features are used to characterize the local quality of DIBR-synthesized image, respectively. For the global sharpness we consider the Just Notice Difference (JND) model of human eyes and use it to extract the JND-based global difference for analyzing the global quality. Finally, we combine the holes distortion evaluation, strip distortion evaluation and global quality to infer the overall perceptual quality. Extensive experiments indicate that our method achieves higher accuracy of quality prediction than most competing metrics. DIBR synthesis distortions quality evaluation view synthesis perceptual quality Electrical engineering. Electronics. Nuclear engineering Yue Zhao verfasserin aut Xu Ma verfasserin aut Sumin Qi verfasserin aut Weiqing Yan verfasserin aut Hua Chen verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 27938-27948 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:27938-27948 https://doi.org/10.1109/ACCESS.2020.2971995 kostenfrei https://doaj.org/article/cd1767ee0df34d51b30ed02843ab7c8f kostenfrei https://ieeexplore.ieee.org/document/8985347/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 27938-27948
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source	In IEEE Access 8(2020), Seite 27938-27948 volume:8 year:2020 pages:27938-27948
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authorswithroles_txt_mv	Laihua Wang @@aut@@ Yue Zhao @@aut@@ Xu Ma @@aut@@ Sumin Qi @@aut@@ Weiqing Yan @@aut@@ Hua Chen @@aut@@
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callnumber-first	T - Technology
author	Laihua Wang
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topic_title	TK1-9971 Quality Assessment for DIBR-Synthesized Images With Local and Global Distortions DIBR synthesis distortions quality evaluation view synthesis perceptual quality
topic	misc TK1-9971 misc DIBR misc synthesis distortions misc quality evaluation misc view synthesis misc perceptual quality misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc DIBR misc synthesis distortions misc quality evaluation misc view synthesis misc perceptual quality misc Electrical engineering. Electronics. Nuclear engineering
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title	Quality Assessment for DIBR-Synthesized Images With Local and Global Distortions
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title_full	Quality Assessment for DIBR-Synthesized Images With Local and Global Distortions
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title_sort	quality assessment for dibr-synthesized images with local and global distortions
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title_auth	Quality Assessment for DIBR-Synthesized Images With Local and Global Distortions
abstract	Depth-Image-Based-Rendering (DIBR), as one important technique in 3D video system, can be used to generate virtual views. Unfortunately, the DIBR algorithms will introduce various distortions and induce an annoying viewing experience. And it has been proved that traditional 2D assessment quality metrics are not suitable for the DIBR-synthesized views. In this paper, we propose a novel approach to assess the quality of DIBR-synthesized images. The proposed method mainly considers three kinds of DIBR-related distortions: holes distortion, strip-sharped distortion and global sharpness. Holes and strip distortions as two local features are used to characterize the local quality of DIBR-synthesized image, respectively. For the global sharpness we consider the Just Notice Difference (JND) model of human eyes and use it to extract the JND-based global difference for analyzing the global quality. Finally, we combine the holes distortion evaluation, strip distortion evaluation and global quality to infer the overall perceptual quality. Extensive experiments indicate that our method achieves higher accuracy of quality prediction than most competing metrics.
abstractGer	Depth-Image-Based-Rendering (DIBR), as one important technique in 3D video system, can be used to generate virtual views. Unfortunately, the DIBR algorithms will introduce various distortions and induce an annoying viewing experience. And it has been proved that traditional 2D assessment quality metrics are not suitable for the DIBR-synthesized views. In this paper, we propose a novel approach to assess the quality of DIBR-synthesized images. The proposed method mainly considers three kinds of DIBR-related distortions: holes distortion, strip-sharped distortion and global sharpness. Holes and strip distortions as two local features are used to characterize the local quality of DIBR-synthesized image, respectively. For the global sharpness we consider the Just Notice Difference (JND) model of human eyes and use it to extract the JND-based global difference for analyzing the global quality. Finally, we combine the holes distortion evaluation, strip distortion evaluation and global quality to infer the overall perceptual quality. Extensive experiments indicate that our method achieves higher accuracy of quality prediction than most competing metrics.
abstract_unstemmed	Depth-Image-Based-Rendering (DIBR), as one important technique in 3D video system, can be used to generate virtual views. Unfortunately, the DIBR algorithms will introduce various distortions and induce an annoying viewing experience. And it has been proved that traditional 2D assessment quality metrics are not suitable for the DIBR-synthesized views. In this paper, we propose a novel approach to assess the quality of DIBR-synthesized images. The proposed method mainly considers three kinds of DIBR-related distortions: holes distortion, strip-sharped distortion and global sharpness. Holes and strip distortions as two local features are used to characterize the local quality of DIBR-synthesized image, respectively. For the global sharpness we consider the Just Notice Difference (JND) model of human eyes and use it to extract the JND-based global difference for analyzing the global quality. Finally, we combine the holes distortion evaluation, strip distortion evaluation and global quality to infer the overall perceptual quality. Extensive experiments indicate that our method achieves higher accuracy of quality prediction than most competing metrics.
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title_short	Quality Assessment for DIBR-Synthesized Images With Local and Global Distortions
url	https://doi.org/10.1109/ACCESS.2020.2971995 https://doaj.org/article/cd1767ee0df34d51b30ed02843ab7c8f https://ieeexplore.ieee.org/document/8985347/ https://doaj.org/toc/2169-3536
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