Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
A Global-Local Approach to Extracting Deformable Fashion Items from Web Images Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Objective Quality Assessment of Screen Content Images by Structure Information

verfasst von : Yuming Fang, Jiebin Yan, Jiaying Liu, Shiqi Wang, Qiaohong Li, Zongming Guo

Erschienen in: Advances in Multimedia Information Processing - PCM 2016

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

In this paper, we propose a novel full-reference objective quality assessment metric of screen content images by structure information. The input screen content image is first divided into textual and pictorial regions. The visual quality of textual regions is predicted based on perceptual structural similarity, where the gradient information is used as the feature. To estimate the visual quality of pictorial regions, we extract the luminance and structure features as feature representation. The overall quality of the screen content image is measured by fusing those of textual and pictorial parts. Experimental results show that the proposed method can obtain better performance of visual quality prediction of SCIs than other existing ones.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel GIP: Generic Image Prior for No Reference Image Quality Assessment
Nächstes Kapitel CrowdTravel: Leveraging Heterogeneous Crowdsourced Data for Scenic Spot Profiling and Recommendation
Literatur
1.
Chang, T., Li, Y.: Deep shot: a framework for migrating tasks across devices using mobile phone cameras. In: ACM Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (2011)
2.
Lu, Y., Li, S., Shen, H.: Virtualized screen: a third element for cloud-mobile convergence. IEEE Multimedia 18(2), 4–11 (2011)CrossRef
3.
Hu, H., Wen, Y., Chua, T.-S., Li, X.: Toward scalable systems for big data analytics: a technology toturial. IEEE Access 2, 652–687 (2014)CrossRef
4.
Lan, C., Shi, G., Wu, F.: Compress compound images in H.264/MPGE-4 AVC by exploiting spatial correlation. IEEE Trans. Image Process. 19, 946–957 (2010)MathSciNetCrossRef
5.
Yang, H., Fang, Y., Lin, W.: Perceptual quality assessment of screen content images. IEEE Trans. Image Process. 24(11), 4408–4421 (2015)MathSciNetCrossRef
6.
Minaee, S., Wang, Y.: Screen content image segmentation using least absolute deviation fitting. In: IEEE International Conference on Image Processing (2015)
7.
Lei, J., Li, S., Zhu, C., Sun, M.-T., Hou, C.: Depth coding based on depth-texture motion and structure similarities. IEEE Trans. Circ. Syst. Video Technol. 25(2), 275–286 (2015)CrossRef
8.
Lei, J., Sun, J., Pan, Z., Kwong, S., Duan, J., Hou, C.: Fast mode decision using inter-view and inter-component correlations for multiview depth video coding. IEEE Trans. Indus. Inf. 11(4), 978–986 (2015)CrossRef
9.
Ma, S., Zhang, X., Zhang, J., Jia, C., Wang, S., Gao, W.: Nonlocal in-loop filter: the way toward next-generation video coding? IEEE MultiMedia 23(2), 16–26 (2016)MathSciNetCrossRef
10.
Wu, J., Lin, W., Shi, G., Liu, A.: Reduced-reference image quality assessment with visual information fidelity. IEEE Trans. Multimedia 12(7), 1700–1705 (2013)CrossRef
11.
Gu, K., Zhai, G., Lin, W., Yang, X., Zhang, W.: No-reference image sharpness assessment in autoregressive parameter space. IEEE Trans. Image Process. 24(10), 3218–3231 (2015)MathSciNetCrossRef
12.
Fang, Y., Ma, K., Wang, Z., Lin, W., Fang, Z., Zhai, G.: No-reference quality assessment for contrast-distorted images based on natural scene statistics. IEEE Signal Process. Lett. 22(7), 838–842 (2015)
13.
Li, L., Lin, W., Wang, X., Yang, G., Bahrami, K., Kot, A.C.: No-reference image blur assessment based on discrete orthogonal moments. IEEE Trans. Cybern. 46(1), 39–50 (2016)CrossRef
14.
Lin, W., Kuo, C.C.J.: Perceptual visual quality metric: a survey. J. Vis. Commun. Image Represent. 22(4), 297–312 (2011)CrossRef
15.
Wang, Z., Bovik, A.C.: Mean squared error: love it or leave it? a new look at fidelity measures. IEEE Signal Process. Mag. 26(1), 98–117 (2009)CrossRef
16.
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRef
17.
Sheikh, H.R., Bovik, A.C.: Image information and visual quality. IEEE Trans. Image Process. 15(2), 430–444 (2006)CrossRef
18.
Wu, J., Lin, W., Shi, G., Liu, A.: Perceptual quality metric with internal generative mechanism. IEEE Trans. Image Process. 22(1), 43–54 (2013)MathSciNetCrossRef
19.
Liu, A., Lin, W., Narwaria, M.: Image quality assessment based on gradient similarity. IEEE Trans. Image Process. 21(4), 1500–1512 (2012)MathSciNetCrossRef
20.
Yang, H., Lin, W., Deng, C.: Image acitivity measure (IAM) for screen image segmentation In: IEEE International Conference on Image Processing (2012)
21.
Zhang, L., Zhang, L., Bovik, A.: A feature-enriched completely blind image quality evaluator. IEEE Trans. Image Process. 24(8), 2579–2591 (2015)MathSciNetCrossRef
22.
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefMATH
23.
Sheikh, H.R., Bovik, A.C., de Veciana, G.: An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Trans. Image Process. 14(12), 2117–2128 (2005)CrossRef
24.
Xue, W., Zhang, L., Mou, X., Bovik, A.C.: Gradient magnitude similarity deviation: a highly efficient perceptual image quality index. IEEE Trans. Image Process. 23(2), 684–695 (2014)MathSciNetCrossRef
25.
Larson, E.C., Chandler, D.M.: Most apparent distortion: full reference image quality assessment and the role of strategy. J. Electron. Imaging 19(1) (2010). 011006
Metadaten
Titel
Objective Quality Assessment of Screen Content Images by Structure Information
verfasst von
Yuming Fang
Jiebin Yan
Jiaying Liu
Shiqi Wang
Qiaohong Li
Zongming Guo
Copyright-Jahr
2016
Buch
Advances in Multimedia Information Processing - PCM 2016

Print ISBN: 978-3-319-48895-0

Electronic ISBN: 978-3-319-48896-7

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-48896-7_60

Neuer Inhalt

    Bildnachweise