lossy compression and iterative reconstruction for encrypted image

1. Lossy Compression and Iterative
Reconstruction for Encrypted
Image
Presented By : Ganesh Gunjal
Guided by : Prof.Neeta A. Deshpande

2. Overview
• Introduction
• Literature Survey
• Proposed Scheme
• Conclusions and Future Scope
• References

3. Introduction

4. Literature Survey
Sr.No
Year
Author
Technique used
Description
1
1963
R.G. Gallgar
Low –density
parity –
check(LDPC)
Original binary image is
encrypted by adding
pseudorandom string and
encrypted data are compressed
by finding the syndromes with
respect to LDPC.
2
2004
M . Johnson &
P. Ishwar
Gaussian Sequence
(Lossy as well as
Lossless
Compression)
Performance of compressing
encrypted image data is as good
as compressing non encrypted
image data.
3
2008
R. Lazzereti &
M. Barni
Low –density
parity –
check(LDPC)
Used for lossless compression of
gray value as well as color
encrypted images.

5. Literature Survey Contd….
Sr.No
Year
Author
Technique used
Description
3
2008
D. Schonbarg
Turbo Codes
Most significant bits (MSB) of
gray value encrypted image are
compressed same technique can
be extended for video
compression.
4
2009
A. Kumar &
A. Makur
compressive
sensing
techniques
Used to achieve lossy
compression of encrypted image
data same algorithm can be
extended for joint
decompression and decryption.

6. The Proposed Scheme

7. Image Encryption

8. Compression

9. Compression Contd…..

10. Compression Contd…..

11. Compression Contd…..

12. Compression Contd…..

13. Image Reconstruction
Where

14. Conclusions and Future Scope
Proposed scheme compresses encrypted image.
Proposed scheme only shuffles pixel position .
Higher the compression ratio and smoother the original image
gives better quality reconstructed image.
In future lossy compression for encrypted image can be
implemented more secure methods.

15. References
[1] M. Johnson, P. Ishwar, V. M. Prabhakaran, D. Schonberg, and K.
Ramchandran, “On compressing encrypted data,” IEEE Trans. Signal
Process., vol. 52, no. 10, pt. 2, pp. 2992–3006, Oct. 2004.
[2] R. G. Gallager, “Low Density Parity Check Codes,” Ph.D.
dissertation, Mass. Inst. Technol., Cambridge, MA, 1963.
[3] D. Schonberg, S. C. Draper, and K. Ramchandran, “On blind
compression of encrypted correlated data approaching the source
entropy rate,” in Proc. 43rd Annu. Allerton Conf., Allerton, IL, 2005.
[4] R. Lazzeretti and M. Barni, “Lossless compression of encrypted
grey-level and color images,” in Proc.16th Eur. Signal Processing
Conf. (EUSIPCO 2008), Lausanne, Switzerland, Aug. 2008 [Online].

16. References Contd…..
[5]W. Liu, W. Zeng, L. Dong, and Q. Yao, “Efficient compression of
encrypted grayscale images,” IEEE Trans. Image Process., vol. 19, no. 4,
pp. 1097–1102, Apr. 2010.
[6] D. Schonberg, S. C. Draper, C. Yeo, and K. Ramchandran, “Toward
compression of encrypted images and video sequences,” IEEE Trans. Inf.
Forensics Security, vol. 3, no. 4, pp. 749–762, Dec. 2008.
[7] A. Kumar and A. Makur, “Lossy compression of encrypted image
by compressing sensing technique,” in Proc. IEEE Region 10
Conf.(TENCON 2009), 2009, pp. 1–6.
[8] T. Bianchi, A. Piva, and M. Barni, “Composite signal representation
for fast and storage-efficient processing of encrypted signals,” IEEE
Trans. Inf. Forensics Security, vol. 5, no. 1, pp. 180–187, Mar. 2010.

17. Thank You…..