![Literature Analysis
Reference Basic concept Database Performance
Evaluation parameters
Claim by Authors Our Findings
[1] Xinpeng Zhang,
Yanli Ren,Liquan
Shen, Zhenxing
Qian, Guorui Feng-
Aug. 2014
Image Compressing
with Encryption
Gray-scale Image Compression ratio A scheme with
improved
compression
performance and
reduced
computational
complexity.
Compression
approach is not
compatible with other
encryption methods
like standard stream
cipher or AES/DES.
[2] M. Johnson, P.
Ishwar, V. M.
Prabhakaran, D.
Schonberg, and K.
Ramchandran, Oct.
2004
Transmitting data
over an insecure and
bandwidth-
constrained channel
by applying
Compression prior
to Encryption.
Redundant Data. Channel Bandwidth,
Frequency,
Compression
efficiency,
encryption
efficiency,
encryption and
compression rate.
The encrypted
data can be
compressed to the
same rate as the
original,
unencrypted
data could have
been compressed.
The approach can be
extend in future for
multimedia data like
images and videos.
[3] Z. Erkin, A. Piva, S.
Katzenbeisser, R. L.
Lagendijk, J.
Shokrollahi, G. Neven,
and M. Barni, 2007
Protecting and
Retrieving of Encrypted
Multimedia Content
JPEG2000 bit streams Channel Bandwidth,
Frequency, encryption
efficiency, encryption
and compression rate.
The study focuses on
cryptography used in
existing solutions
to processing of
encrypted signals.
The study focuses on
analysis and retrieval
Of multimedia content, as
well as multimedia
content protection
domains of secure signal
processing.
Project Seminar-I](https://image.slidesharecdn.com/presentationformat1-221011163531-3d693b87/85/Presentation_Format-1-ppt-5-320.jpg)
![Reference Basic concept Database Performance
Evaluation
parameters
Claim by Authors Our Findings
[4] G. Jakimoski and K.
P. Subbalakshmi, 2007
Security constrains in
Compressing Encrypted
Data
Redundant Data Time , compression
ratio.
The
compression does not
compromise the security
of the system.
The approach can be
extend in future for
multimedia data like
images and videos.
[5] R. Lazzeretti and M.
Barni, Aug 2008.
Lossless compression of
encrypted grey-level and
color images
Grey-level and color
images
Bit rates, compression
ratio.
Spatial de-correlation,
working on the
prediction error gives
much better results
The results obtained are
promising, even if the
gap to state-of-the-art
compression in the plain
domain is still large.
[6] A. Kumar and A.
Makur, 2008.
Distributed source
coding based
encryption and
lossless compression
of gray scale and
color images
gray scale and color
images
Prediction error, bit
rate, compression
ratio.
An improvement on
the compression
gains of RGB color
images was obtained
by exploiting the
correlation
among the planes.
Both in terms
of computational
cost and lossless
compression it is
always advantageous
to apply encryption
on the prediction
errors
instead of applying
encryption directly
on the image.
Literature Analysis
Project Seminar-I](https://image.slidesharecdn.com/presentationformat1-221011163531-3d693b87/85/Presentation_Format-1-ppt-6-320.jpg)
![References
[1] Xinpeng Zhang; Yanli Ren; Liquan Shen; Zhenxing Qian; Guorui Feng, "Compressing Encrypted Images With Auxiliary
Information," Multimedia, IEEE Transactions on , vol.16, no.5, pp.1327,1336, Aug. 2014
[2] M. Johnson, P. Ishwar, V. M. Prabhakaran, D. Schonberg, and K. Ramchandran, “On compressing encrypted data,”
IEEE Trans. Signal Process., vol. 52, no. 10, pp. 2992–3006, Oct. 2004.
[3] Z. Erkin, A. Piva, S. Katzenbeisser, R. L. Lagendijk, J. Shokrollahi, G. Neven, and M. Barni, “Protection and retrieval of
encrypted multimedia content: When cryptography meets signal processing,” EURASIP J. Inf. Security, pp. 1–20, 2007.
[4] G. Jakimoski and K. P. Subbalakshmi, “Security of compressing encrypted sources,” in Proc. 41st Asilomar Conf.
Signals, Systems and Computers (ACSSC 2007), 2007, pp. 901–903.
[5] R. Lazzeretti and M. Barni, “Lossless compression of encrypted greylevel and color images,” in Proc. 16th Eur. Signal
Processing Conf. (EUSIPCO 2008), Lausanne, Switzerland, Aug. 2008.
[6] A. Kumar and A. Makur, “Distributed source coding based encryption and lossless compression of gray scale and color
images,” in Proc. IEEE 10th Workshop Multimedia Signal Processing, 2008, pp. 760–764.
[7] W. Liu, W. Zeng, L. Dong, and Q. Yao, “Efficient compression of encrypted grayscale images,” IEEE Trans. Signal
Process, vol. 19, no. 4, pp. 1097–1102, Apr. 2010.
[8] 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, 2008.
[9] D. Klinc, C. Hazayy, A. Jagmohan, H. Krawczyk, and T. Rabinz, “On compression of data encrypted with block ciphers,”
in Proc. IEEE Data Compression Conf. (DCC ’09), 2009, pp. 213–222.
[10] E. J. Candes and M. B. Wakin, “An introduction to compressive sampling,” IEEE Signal Process. Mag., vol. 25, no. 2,
pp. 21–30, Mar-2008.
Project Seminar-I](https://image.slidesharecdn.com/presentationformat1-221011163531-3d693b87/85/Presentation_Format-1-ppt-11-320.jpg)
Presentation_Format (1).ppt
- 1. A Scheme for Encrypted Image Data Compression Guide Dr. D. G. Harkut Department of Computer Science and Engineering Prof Ram Meghe College of Engineering and Management Badnera-Amravati, India. 2022-2023 By: A. B. Xyssss (19) S. F. Bdfjjjjj (23) A. B. Xyssss (07) S. F. Bdfjjjjj (67)
- 2. Content Introduction Literature Analysis Problem Definition Proposed Approach / Basic Idea / Algorithm Technology Used Advantages / Disadvantages of this approach and Conclusion References Project Seminar-I
- 3. Introduction Image Compression: •The objective of Image Compression is to reduce irrelevance and redundancy of the image data in order to be able to store or transmit data in an efficient form. • Types of image Compression are: •Lossless Image Compression: •Lossy Image Compression Image Encryption: • Image encryption techniques try to convert original image to another image that is hard to understand; to keep the image confidential between users, in other word, it is essential that nobody should get to know the content without a key for decryption. Project Seminar-I
- 4. Introduction Image Compression Standards: •Different compression standard exists • Image Compression Standrds are: •JPEG: Encryption Standards: • Symmetric Encryption • Asymmetric Encryption Project Seminar-I
- 5. Literature Analysis Reference Basic concept Database Performance Evaluation parameters Claim by Authors Our Findings [1] Xinpeng Zhang, Yanli Ren,Liquan Shen, Zhenxing Qian, Guorui Feng- Aug. 2014 Image Compressing with Encryption Gray-scale Image Compression ratio A scheme with improved compression performance and reduced computational complexity. Compression approach is not compatible with other encryption methods like standard stream cipher or AES/DES. [2] M. Johnson, P. Ishwar, V. M. Prabhakaran, D. Schonberg, and K. Ramchandran, Oct. 2004 Transmitting data over an insecure and bandwidth- constrained channel by applying Compression prior to Encryption. Redundant Data. Channel Bandwidth, Frequency, Compression efficiency, encryption efficiency, encryption and compression rate. The encrypted data can be compressed to the same rate as the original, unencrypted data could have been compressed. The approach can be extend in future for multimedia data like images and videos. [3] Z. Erkin, A. Piva, S. Katzenbeisser, R. L. Lagendijk, J. Shokrollahi, G. Neven, and M. Barni, 2007 Protecting and Retrieving of Encrypted Multimedia Content JPEG2000 bit streams Channel Bandwidth, Frequency, encryption efficiency, encryption and compression rate. The study focuses on cryptography used in existing solutions to processing of encrypted signals. The study focuses on analysis and retrieval Of multimedia content, as well as multimedia content protection domains of secure signal processing. Project Seminar-I
- 6. Reference Basic concept Database Performance Evaluation parameters Claim by Authors Our Findings [4] G. Jakimoski and K. P. Subbalakshmi, 2007 Security constrains in Compressing Encrypted Data Redundant Data Time , compression ratio. The compression does not compromise the security of the system. The approach can be extend in future for multimedia data like images and videos. [5] R. Lazzeretti and M. Barni, Aug 2008. Lossless compression of encrypted grey-level and color images Grey-level and color images Bit rates, compression ratio. Spatial de-correlation, working on the prediction error gives much better results The results obtained are promising, even if the gap to state-of-the-art compression in the plain domain is still large. [6] A. Kumar and A. Makur, 2008. Distributed source coding based encryption and lossless compression of gray scale and color images gray scale and color images Prediction error, bit rate, compression ratio. An improvement on the compression gains of RGB color images was obtained by exploiting the correlation among the planes. Both in terms of computational cost and lossless compression it is always advantageous to apply encryption on the prediction errors instead of applying encryption directly on the image. Literature Analysis Project Seminar-I
- 7. Problem Definition After analyzing the literature, following problem definition is formulated: Data compression plays a very important role in the information transmission domain. In transmission of information, security is the key issue. To address the issue of security and optimal use of bandwidth, there is a need of some mechanism which can perform both these tasks. Here, the objective is to develop a scheme to perform compression of encrypted image data. Firstly, the encryption mechanism will be developed and later the compression mechanism. Project Seminar-I
- 8. Proposed Approach / Basic Idea / Algorithm Technology Used Advantages / Disadvantages of this approach and Conclusion References Project Seminar-I
- 9. Technology Used Advantages / Disadvantages of this approach and Conclusion References Project Seminar-I
- 11. References [1] Xinpeng Zhang; Yanli Ren; Liquan Shen; Zhenxing Qian; Guorui Feng, "Compressing Encrypted Images With Auxiliary Information," Multimedia, IEEE Transactions on , vol.16, no.5, pp.1327,1336, Aug. 2014 [2] M. Johnson, P. Ishwar, V. M. Prabhakaran, D. Schonberg, and K. Ramchandran, “On compressing encrypted data,” IEEE Trans. Signal Process., vol. 52, no. 10, pp. 2992–3006, Oct. 2004. [3] Z. Erkin, A. Piva, S. Katzenbeisser, R. L. Lagendijk, J. Shokrollahi, G. Neven, and M. Barni, “Protection and retrieval of encrypted multimedia content: When cryptography meets signal processing,” EURASIP J. Inf. Security, pp. 1–20, 2007. [4] G. Jakimoski and K. P. Subbalakshmi, “Security of compressing encrypted sources,” in Proc. 41st Asilomar Conf. Signals, Systems and Computers (ACSSC 2007), 2007, pp. 901–903. [5] R. Lazzeretti and M. Barni, “Lossless compression of encrypted greylevel and color images,” in Proc. 16th Eur. Signal Processing Conf. (EUSIPCO 2008), Lausanne, Switzerland, Aug. 2008. [6] A. Kumar and A. Makur, “Distributed source coding based encryption and lossless compression of gray scale and color images,” in Proc. IEEE 10th Workshop Multimedia Signal Processing, 2008, pp. 760–764. [7] W. Liu, W. Zeng, L. Dong, and Q. Yao, “Efficient compression of encrypted grayscale images,” IEEE Trans. Signal Process, vol. 19, no. 4, pp. 1097–1102, Apr. 2010. [8] 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, 2008. [9] D. Klinc, C. Hazayy, A. Jagmohan, H. Krawczyk, and T. Rabinz, “On compression of data encrypted with block ciphers,” in Proc. IEEE Data Compression Conf. (DCC ’09), 2009, pp. 213–222. [10] E. J. Candes and M. B. Wakin, “An introduction to compressive sampling,” IEEE Signal Process. Mag., vol. 25, no. 2, pp. 21–30, Mar-2008. Project Seminar-I
- 12. Thank You………