CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
Reseach Article

Study on Aesthetic Analysis of Photographic Images Techniques to Produce High Dynamic Range Images

by Vikram More, Poorva Agrawal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 159 - Number 8
Year of Publication: 2017
Authors: Vikram More, Poorva Agrawal
10.5120/ijca2017913020

Vikram More, Poorva Agrawal . Study on Aesthetic Analysis of Photographic Images Techniques to Produce High Dynamic Range Images. International Journal of Computer Applications. 159, 8 ( Feb 2017), 34-38. DOI=10.5120/ijca2017913020

@article{ 10.5120/ijca2017913020,
author = { Vikram More, Poorva Agrawal },
title = { Study on Aesthetic Analysis of Photographic Images Techniques to Produce High Dynamic Range Images },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2017 },
volume = { 159 },
number = { 8 },
month = { Feb },
year = { 2017 },
issn = { 0975-8887 },
pages = { 34-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume159/number8/27024-2017913020/ },
doi = { 10.5120/ijca2017913020 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:05:15.877283+05:30
%A Vikram More
%A Poorva Agrawal
%T Study on Aesthetic Analysis of Photographic Images Techniques to Produce High Dynamic Range Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 159
%N 8
%P 34-38
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recent Advancements in image acquisition as well as visual computing leads easy and cheap availability of technology. Additionally, it’s more user friendly for average user. Using such new technologies end users expected to be more appealing images. However, producing the appealing images needs also needs the enough knowledge of aesthetic principles during process of acquisition and editing. The average user does not have complete training and experience of doing such tasks. Therefore, it is required to have automatic method in which modelling of aesthetic principles and building systems that can generate aesthetic signature to generate more appealing images. There are number of methods introduced under different categories for automated aesthetic analysis of photographic images with goal of generating more appealing images such as HDR. In this paper, first we are presenting details on Aesthetic Quality Assessment and Attributes, then different aesthetic analysis methods have been studied. The information of different camera technology is discussed in this paper. The comparative study of all reviewed method is presented with accuracy analysis to end this paper

References
  1. R. Datta, D. Joshi, J. Li, and J. Z. Wang, “Studying aesthetics in photographic images using a computational approach,” in Proc. of ECCV, 2006, pp. 7–13.
  2. Y. Ke, X. Tang, and F. Jing, “The design of high-level features for photo quality assessment.” in in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2006, pp. 419–426.
  3. Y. Luo and X. Tang, “Photo and video quality evaluation: Focusing on the subject,” in Proceedings of the 10th European Conference on Computer Vision (ECCV), 2008, pp. 386–399.
  4. M. Nishiyama, T. Okabe, I. Sato, and Y. Sato, “Aesthetic quality classification of photographs based on color harmony,” in Proc. of CVPR, 2011, pp. 33–40.
  5. F. Perronnin, “Ava: A large-scale database for aesthetic visual analysis,” in IEEE CVPR, 2012, pp. 2408–2415.
  6. C. Chen, W. Chen, and J. A. Bloom, “A universal reference-free blurriness measure,” in SPIE vol. 7867, 2011.
  7. S. Daly and X. Feng, “Decontouring: Prevention and removal of false contour artifacts,” in Proc. of Human Vision and Electronic Imaging IX, ser. SPIE, vol. 5292, 2004, pp. 130–149.
  8. S. Winkler, “Perceptual video quality metrics – a review,” in Digital Video Image Quality and Perceptual Coding, H. R. Wu and K. R. Rao, Eds. CRC Press 2006, 2006, pp. 155–179.
  9. H. Sheikh, A. Bovik, and L. Cormack, “No-reference quality assessment using natural scene statistics: JPEG2000,” IEEE Trans. on Image Processing, vol. 14, no. 11, pp. 1918 –1927, 2005.
  10. J. Kopf, W. Kienzle, S. Drucker, and S. B. Kang, “Quality prediction for image completion,” ACM Trans. on Graphics (Proc. of SIGGRAPH), vol. 31, no. 6, pp. 131:1–131:8, 2012.
  11. G. Ramanarayanan, J. Ferwerda, B. Walter, and K. Bala, “Visual equivalence: towards a new standard for image fidelity,” ACM Transactions on Graphics (Proc. of SIGGRAPH), vol. 26, 2007.
  12. J. Kˇriv´anek, J. A. Ferwerda, and K. Bala, “Effects of global illumination approximations on material appearance,” ACM Trans. on Graphics (Proc. of SIGGRAPH), vol. 29, pp. 112:1–10, 2010.
  13. Xin Lu, Zhe Lin, Jian chaoYang, James Z wang,” RAPID: RAting PIctorial aesthetic using Deep learning”, ACM 2014
  14. Christel Chamaret and Fabrice Urban, “No Reference Harmony-guided Quality Assessment”, IEEE Conference on Computer Vision and Pattern Recognition Workshops 2013.
  15. Tsung-Jung Liu, Weisi Lin, C.-C. Jay Kuo,” image quality assessment using multi method fusion “, IEEE Transactions on Image processing, VOL.22, No.5, May 2013
  16. Anish Mittal, Rajiv Soundararajan and Alan C Bovik, “Making a completely blind image quality analyzer “, IEEE 2012.
  17. Luca Marchesotti, Florent Perronnin, Diane Larlus, Gabriela Csurka, “Assessing the aesthetic quality of photographs using generic image descriptors”, IEEE international conference on computer vision 2011.
  18. Hsiao-Hang, Su, Tse-wei chen, Chieh-chi kao, Winston H Hsu, Shao-Yi Chien, “Scenic photo quality assessment with bag of aesthetic –preserving features”, ACM 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Aesthetic Analysis Image Acquisition Aesthetic Signature HDR Image Image Quality Photographic Image.