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Photo and Video Quality Evaluation: Focusing on the Subject

  • Yiwen Luo
  • Xiaoou Tang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5304)

Abstract

Traditionally, distinguishing between high quality professional photos and low quality amateurish photos is a human task. To automatically assess the quality of a photo that is consistent with humans perception is a challenging topic in computer vision. Various differences exist between photos taken by professionals and amateurs because of the use of photography techniques. Previous methods mainly use features extracted from the entire image. In this paper, based on professional photography techniques, we first extract the subject region from a photo, and then formulate a number of high-level semantic features based on this subject and background division. We test our features on a large and diverse photo database, and compare our method with the state of the art. Our method performs significantly better with a classification rate of 93% versus 72% by the best existing method. In addition, we conduct the first study on high-level video quality assessment. Our system achieves a precision of over 95% in a reasonable recall rate for both photo and video assessments. We also show excellent application results in web image search re-ranking.

Keywords

Video Quality Image Quality Assessment Color Combination Vertical Derivative Video Quality Assessment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Wang, Z., Sheikh, H.R., Bovik, A.C.: No-reference perceptual quality assessment of JPEG compressed images. ICIP (2002)Google Scholar
  2. 2.
    Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Processing 13 (2004)Google Scholar
  3. 3.
    Sheikh, H., Bovik, A., de Veciana, G.: An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Trans. Image Processing 14 (2005)Google Scholar
  4. 4.
    Tong, H., Li, M., Zhang, H., He, J., Zhang, C.: Classification of Digital Photos Taken by Photographers or Home Users. In: Proc. Pacific-Rim Conference on Multimedia (2004)Google Scholar
  5. 5.
    Datta, R., Joshi, D., Li, J., Wang, J.: Studying Aesthetics in Photographic Images Using a Computational Approach. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951. Springer, Heidelberg (2006)Google Scholar
  6. 6.
    Ke, Y., Tang, X., Jing, F.: The Design of High-Level Features for Photo Quality Assessment. In: CVPR (2006)Google Scholar
  7. 7.
    Freeman, M.: The Complete Guide to Light and Lighting. Ilex Press (2007)Google Scholar
  8. 8.
    Freeman, M.: The Photographer’s Eye: Composition and Design for Better Digital Photos. Ilex Press (2007)Google Scholar
  9. 9.
    London, B., Upton, J., Stone, J., Kobre, K., Brill, B.: Photography, 8th edn. Pearson Prentice Hall, London (2005)Google Scholar
  10. 10.
    Itten, J.: Design and Form: The Basic Course at the Bauhaus and Later. Wiley, Chichester (1975)Google Scholar
  11. 11.
    Manav, B.: Color-Emotion Associations and Color Preferences: A Case Study for Residences. Color Research and Application 32 (2007)Google Scholar
  12. 12.
    Gao, X., Xin, J., Sato, T., Hansuebsai, A., Scalzo, M., Kajiwara, K., Guan, S., Valldeperas, J., Lis, M., Billger, M.: Analysis of Cross-Cultural Color Emotion. Color Research and Application 32 (2007)Google Scholar
  13. 13.
    Levin, A.: Blind motion deblurring using image statistics. In: NIPS (2006)Google Scholar
  14. 14.
    Tokumaru, M., Muranaka, N., Imanishi, S.: Color design support system considering color harmony. In: Proc. of the 2002 IEEE International Conference on Fuzzy Systems, vol. 1 (2002)Google Scholar
  15. 15.
    Cohen-Or, D., Sorkine, O., Gal, R., Leyvand, T., Xu, Y.: Color harmonization. ACM Transactions on Graphics (TOG) 25 (2006)Google Scholar
  16. 16.
    Yan, W., Kankanhalli, M.: Detection and removal of lighting & shaking artifacts in home videos. In: Proc. of the tenth ACM international conference on Multimedia (2002)Google Scholar
  17. 17.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (2000)CrossRefMATHGoogle Scholar
  18. 18.
    Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors). Ann. Statist 28 (2000)Google Scholar
  19. 19.
    Cui, J., Wen, F., Tang, X.: Real Time Google and Live Image Search Re-ranking. In: Proc. of ACM international conference on Multimedia (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Yiwen Luo
    • 1
  • Xiaoou Tang
    • 1
  1. 1.Department of Information EngineeringThe Chinese University of Hong KongHong Kong

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