Skip to main content
SpringerLink
Log in

Local Feature-Based Photo Album Compression by Eliminating Redundancy of Human Partition

  • Conference paper
  • First Online:
Computer Vision – ACCV 2016 Workshops (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10116))

Included in the following conference series:

  • 1774 Accesses

Abstract

With the explosive growth of photo uploading on the web, traditional photo album compression using individual image coding is needed to be improved to save the storage spaces. Recently, an advance technique of photo album compression via video compression is proposed which utilizes the similarity between photos to improve the compression performance. In this paper, we modify the original scheme to improve the compression performance when photos containing human beings. Experiment results show that the proposed method outperforms the state-of-the-art method by at most 12.7% of bit-rate savings for compressing photo albums with humans. Comparing with traditional JPEG compression, the proposed method achieves 70% to 85% of bit-rate savings.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Shi, Z., Sun, X., Wu, F.: Feature-based image set compression. In: IEEE ICME, pp. 1–6 (2013)

    Google Scholar 

  2. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model tting with applications to image analysis and automated cartography. ACM Commun. 24, 381–395 (1981)

    Article  Google Scholar 

  3. Shi, Z., Sun, X., Wu, F.: Photo album compression for cloud storage using local features. Emerg. Sel. Top. Circuits Syst. 4, 17–28 (2014)

    Article  Google Scholar 

  4. Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57, 137–154 (2004)

    Article  Google Scholar 

  5. Musatenko, Y.S., Kurashov, V.N.: Correlated image set compression system based on new fast efficient algorithm of Karhunen-Loeve transform, pp. 518–529. International Society for Optics and Photonics (1998)

    Google Scholar 

  6. Karadimitriou, K., Tyler, J.M.: The centroid method for compressing sets of similar images. IEEE Pattern Recogn. Lett. 19, 585–593 (1998)

    Article  Google Scholar 

  7. Ait-Aoudia, S., Gabis, A.: A comparison of set redundancy compression techniques. EURASIP J. Adv. Sig. Process. 2006, 216 (2006)

    Google Scholar 

  8. Yeung, C.H., Au, O.C., Tang, K., Yu, Z., Luo, E., Wu, Y., Tu, S.F.: Compressing similar image sets using low frequency template. In: IEEE ICME, pp. 1–6 (2011)

    Google Scholar 

  9. Chen, C.P., Chen, C.S., Chung, K.L., Lu, H.I., Tang, G.Y.: Image set compression through minimal-cost prediction structure. In: IEEE ICIP, pp. 1289–1292 (2004)

    Google Scholar 

  10. Schmieder, A., Cheng, H., Li, X.: A study of clustering algorithms and validity for lossy image set compression. In: IPCV, pp. 501–506 (2009)

    Google Scholar 

  11. Lu, Y., Wong, T.T., Heng, P.A.: Digital photo similarity analysis in frequency domain and photo album compression. In: 3rd International Conference on Mobile and Ubiquitous Multimedia, pp. 237–244 (2004)

    Google Scholar 

  12. Zou, R., Au, O.C., Zhou, G., Dai, W., Hu, W., Wan, P.: Personal photo album compression and management. In: IEEE ISCAS, pp. 1428–1431 (2013)

    Google Scholar 

  13. Chandrasekhar, V., Takacs, G., Chen, D., Tsai, S.S., Grzeszczuk, R., Girod, B.: CHoG: compressed histogram of gradients a low bit-rate feature descriptor. In: CVPR, pp. 2504–2511 (2009)

    Google Scholar 

  14. Reinhard, E., Ashikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. Comput. Graph. Appl. 21, 34–41 (2001)

    Article  Google Scholar 

  15. Day, W.H.E., Edelsbrunner, H.: Efficient algorithms for agglomerative hierarchical clustering methods. J. Classif. 1, 7–24 (1984)

    Article  MATH  Google Scholar 

  16. https://hevc.hhi.fraunhofer.de/svn/svn_HEVCSoftware

  17. http://cvlabwww.epfl.ch/data/multiview/denseMVS.html

  18. http://www.robots.ox.ac.uk/vgg/data2.html

  19. Chu, Y.J., Liu, T.H.: On the shortest arborescence of a directed graph. Sci. Sinica 14, 1396–1400 (1965). Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE PAMI 23, 1222–1239 (2011)

    MathSciNet  MATH  Google Scholar 

  20. Bossen, F.: Common HM test conditions and software reference configurations. In: JCTVC-L1100 (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan

    Chia-Hsin Chan, Bo-Hsyuan Chen & Wen-Jiin Tsai

Authors
  1. Chia-Hsin Chan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bo-Hsyuan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wen-Jiin Tsai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chia-Hsin Chan .

Editor information

Editors and Affiliations

  1. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Chu-Song Chen

  2. Tsinghua University , Beijing, China

    Jiwen Lu

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kai-Kuang Ma

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Chan, CH., Chen, BH., Tsai, WJ. (2017). Local Feature-Based Photo Album Compression by Eliminating Redundancy of Human Partition. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10116. Springer, Cham. https://doi.org/10.1007/978-3-319-54407-6_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-54407-6_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54406-9

  • Online ISBN: 978-3-319-54407-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation