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Dictionary Design for Block-Based Intra-image Compression

  • Arabinda Sahoo
  • Pranati Das
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 109)

Abstract

A very crucial question in sparse approximation-based image compression is the choice of the dictionary. An effective dictionary can lead to excellent representation of an image with least number of dictionary atoms and lead to excellent image compression. The performance of block-based intra-image compression scheme depends on how well the residuals obtained from intra-prediction are encoded. Transform-based intra-compression method uses fixed DCT dictionary for encoding of prediction residuals. However, DCT dictionary is not suited for efficient encoding of prediction residuals with complex and non-periodic characteristics. This paper presents an algorithm to design an over-complete residual dictionary suitable for encoding prediction residuals. Simulation results demonstrate that the proposed residual dictionary yields superior performance as compared to other standard methods.

Keywords

Sparse approximation Intra-image compression DCT dictionary K-SVD 

References

  1. 1.
    Bryt O, Elad M (2008) Compression of facial images using the K-SVD algorithm. J Vis Commun Image Representation 19(4):270–282CrossRefGoogle Scholar
  2. 2.
    Zepeda J, Guillemot C, Kijak E (2011) Image compression using sparse representations and the iteration-tuned and aligned dictionary. IEEE J Sel Topics Signal Process 5(5):1061–1073CrossRefGoogle Scholar
  3. 3.
    Zhan X, Zhang Y, Huo C (2013) SAR image compression using multiscale dictionary learning and sparse representation. IEEE Geosci Remote Sens Lett 10(5):1090–1094Google Scholar
  4. 4.
    Shao G, Wu Y, Yong A, Liu X, Guo T (2014) Fingerprint compression based on sparse representation. IEEE Trans Image Process 23(2):489–501Google Scholar
  5. 5.
    Zhu J-Y, Wang Z-Y, Zhong R (2015) Dictionary based surveillance image compression. J Vis Commun Image R 31(7):225–230CrossRefGoogle Scholar
  6. 6.
    Sahoo A, Das P (2017) Dictionary based image compression via sparse representation. Int J Electr Comput Eng 7(4):1964–1972CrossRefGoogle Scholar
  7. 7.
    Mallat S, Zhang Z (1993) Matching pursuit with time-frequency dictionaries. IEEE Trans Signal Process 41(12):3397–3415CrossRefGoogle Scholar
  8. 8.
    Pati YC, Rezaiifar R, Krishnaprasad PS (1993) Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. In: Proceedings of the twenty-seventh Asilomar conference on signals, systems and computers, pp 40–44Google Scholar
  9. 9.
    Aharon M, Elad M, Bruckstein A (2006) K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 54(11):4311–4322Google Scholar
  10. 10.
    Turkan M (2012) Novel texture synthesis methods and their application to image prediction and image inpainting, Ph.D. ThesisGoogle Scholar
  11. 11.
  12. 12.
  13. 13.
    Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Arabinda Sahoo
    • 1
  • Pranati Das
    • 2
  1. 1.Department of ECEITER, Siksha ‘O’ Anusandhan Deemed to be UniversityBhubaneswarIndia
  2. 2.Department of Electrical EngineeringIndira Gandhi Institute of TechnologySarangIndia

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