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
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
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
