Semantic Understanding and Task-Oriented for Image Assessment

  • Cheng-Min TsaiEmail author
  • Shin-Shen Guan
  • Wang-Chin Tsai
  • Zhi-Hua Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10926)


This study focuses on image perception issues based on humans’ visual assessment. Semantic understanding and task-oriented are also considered in image assessment. The brightness and colorfulness attributes are selected to be the image assessment tasks in the study. The Linear Regression (LR) analysis and Non-Linear Regression (NLR) analysis methods are used to establish the image assessment models, which also compares their prediction ability. The visual assessment experiment was comprised of 90 participants. Four images were selected from the ISO standard by the focus group. The results showed that “brightness” and “colorfulness” remained stable in the predictive models of the LR and NLR methods. The results also demonstrated very high prediction ability in brightness and colorfulness in both the linear and non-linear models. The brightness attribute directly relates to the image’s lightness, and the colorfulness attribute directly relates to the image’s saturation. The simple semantic understanding and the single task oriented that assessed the brightness and colorfulness of the images are also very important in the image assessment experiment.


Brightness Saturation Visual assessment Linear regression 



The authors would like to thanks TTLA (Taiwan TFT LCD Association) for supporting this research and providing insightful comments. The authors would also like to thanks all observers for helpful in the experiments.


  1. 1.
    Newell, A.: Unified Theories of Cognition. Harvard University Press, Cambridge (1990)Google Scholar
  2. 2.
    Maeder, A.J., Eckert, M.: Medical image compression: quality and performance issues. In: Pham, B., Braun, M., Maeder, A.J, Eckert, M.P. (eds.) New Approaches in Medical Image Analysis, Proceedings of SPIE, vol. 3747, pp. 93–101 (1999)Google Scholar
  3. 3.
    Tsai, Cheng-Min, Guan, Shing-Sheng, Tsai, Wang-Chin: Eye movements on assessing perceptual image quality. In: Zhou, Jia, Salvendy, Gavriel (eds.) ITAP 2016. LNCS, vol. 9754, pp. 378–388. Springer, Cham (2016). Scholar
  4. 4.
    Tsai, C.M., Guan, S.S., Juan, L.Y.G, Lai, Y.Y.: The scale on different physical attributes of images. In: 11th Congress of the International Colour Association on Proceedings, Sydney, Australia (2009)Google Scholar
  5. 5.
    Sheikh, H.R., Bovik, A.C.: Image information and visual quality. IEEE Trans. Image Process. 15(2), 430–444 (2006)CrossRefGoogle Scholar
  6. 6.
    Ginesu, G., Massidda, F., Giusto, D.D.: A multi-factors approach for image quality assessment based on a human visual system model. Sig. Process. Image Commun. 21, 316–333 (2006)CrossRefGoogle Scholar
  7. 7.
    Fedorovskaya, E.A., Ridder, H., Blommaert, F.J.: Chroma variants and perceived quality of color images of natural scenes. Color Res. Appl. 22(2), 96–110 (1996)CrossRefGoogle Scholar
  8. 8.
    Kurita, T., Saito, A.: A characteristic of the temporal integrator in the eye-tracing integration model of the visual system on the perception of displayed moving images. In: IDW 2002 Conference VHF2-1 on Proceedings, pp. 1279–128 (2002)Google Scholar
  9. 9.
    Chalmers, A.N.: Colour difference and colour preference in video imaging. In: 8th Congress of the International Colour Association on Proceedings, Kyoto, Japan, pp. 634–637 (1997)Google Scholar
  10. 10.
    Maeder, A.J.: The image importance approach to human vision based image quality characterization. Pattern Recogn. Lett. 26, 347–354 (2004)CrossRefGoogle Scholar
  11. 11.
    Watson, A.B., Malo, J.: Video quality measures based on the standard spatial observer. In: IEEE ICIP, pp. 41–44 (2002)Google Scholar
  12. 12.
    Janssen, T.J.W.M., Blommaert, F.J.J.: A computational approach to image quality. Displays 21, 129–142 (2000)CrossRefGoogle Scholar
  13. 13.
    Nguyen, A., Chandran, V., Sridharan, S.: Gaze trackign for region of interest coding in JPEG 2000. Sig. Process. Image Commun. 21, 356–377 (2006)Google Scholar
  14. 14.
    Civanlar, M. R.: Content adaptive video coding and transport. In Proceedings of the IEEE 12th Signal Processing and Communications Applications Conference, pp. 28–30 (2004)Google Scholar
  15. 15.
    Oda, K., Yuuki, A., Teragaki, T.: Evaluation of moving picture quality using the pursuit camera system. Euro Display 6(3), 115–118 (2002)Google Scholar
  16. 16.
    Egmont-Petersena, M., de Ridderb, D., Handelsc, H.: Image processing with neural networks - a reivew. Pattern Recogn. Lett. 35, 2279–2301 (2002)CrossRefGoogle Scholar
  17. 17.
    Sheedy, J.E., Smith, R., Hayes, J.: Visual effects of the luminance surrounding a computer display. Ergonomics 48(9), 1114–1128 (2005)CrossRefGoogle Scholar
  18. 18.
    Choi, S.Y., Luo, M.R., Pointer, M.R., Rhodes, P.A.: Investigation of large display color image appearance i: important factors affecting perceived quality. J. Imaging Sci. Technol. 52(4), 040904-1–040904-11 (2008)Google Scholar
  19. 19.
    Choi, S.Y., Luo, M.R., Pointer, M.R., Rhodes, P.A.: Investigation of large display color image appearance ii: the influence of surround conditions. J. Imaging Sci. Technol. 52(4), 040905-1–040905-9 (2008)Google Scholar
  20. 20.
    Choi, S.Y., Luo, M.R., Pointer, M.R., Rhodes, P.A.: Investigation of large display color image appearance- iii: modeling image naturalness. J. Imaging Sci. Technol. 53(3), 301104-1–301104-12 (2009)CrossRefGoogle Scholar
  21. 21.
    Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., Tatham, R.L.: Multivariate Data Analysis, 6th edn. Prentice Hall, Englewood Cliffs (2006)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Cheng-Min Tsai
    • 1
    Email author
  • Shin-Shen Guan
    • 2
  • Wang-Chin Tsai
    • 3
  • Zhi-Hua Zhang
    • 1
  1. 1.Department of Visual Arts and DesignNanhua UniversityChiayiTaiwan, R.O.C.
  2. 2.School of DesignFujian University of TechnologyFuzhouChina
  3. 3.Department and Graduate School of Product and Media DesignFo Guang UniversityYilanTaiwan, R.O.C.

Personalised recommendations