The document discusses LZW compression, an adaptive dictionary-based compression technique developed by Abraham Lempel, Jakob Ziv, and Terry Welch. It presents LZW compression's implementation, advantages like reducing file size and fast speed, and limitations like being suitable only for repetitive data. The document also describes improving LZW compression through techniques like adaptive Huffman coding and bit plane slicing, and applications of LZW compression in fields like medicine, graphics files, and hardware devices.

1. LZW COMPRESSION
Presented By:
Rabia Nazir
12-Mtech-2015
DEPARTMENT OF COMPUTER SCIENCE & IT

2. CONTENTS
 Introduction
 Implementation of lzw compression
 Improving lzw compression
 Advantages
 Limitations
 Applications
 Implementation in matlab
 References

3. ABOUT LZW COMPRESSION
 Abraham Lempel, Jakob Ziv and Terry Welch
 Adaptive dictionary based technique
 If dictionary overflows, add a bit to each code
 Uses greedy approach to divide text
 Encoding: dictionary initialized to contain single
character strings
 Scanning successively larger substrings untill no
match found

4. AMERICAN JOURNAL OF ENGINEERING RESEARCH
LZW DATA COMPRESSION [1]

5. DECODING

6. ADVANTAGES
 Most popular Lossless compression
 Reduces file size containing repetitive data
 No need to pass string table to decompression
 Table can be recreated
 Fast and simple

7. INTERNATIONAL JOURNAL OF INFORMATION SCIENCES AND TECHNIQUES
DESIGN AND IMPLEMENTATION OF LZW IMAGE
COMPRESSION[2]
o Evaluated by finite state machine in VHDL
o Dictionary based on content access memory
o Each character replaced by less number of bits
than its ASCII value
 Simulations by Xilinix ISE simulator synthesises
HDL
 Reduction of storage by 60.25% & increased
compression rate by 30.3%

8. ARCHITECTURE
Finite state machine architecture
Generate code

9. LIMITATIONS OF LZW COMPRESSION
 Suitable for repetitive data only
 Type of image & number of colors must be
considered
 Can’t be used in images with shadows or gradient

10. EUROPEAN JOURNAL OF SCIENTIFIC RESEARCH
IMPROVING LZW IMAGE COMPRESSION [3]
o Focuses on LZW, Adaptive Huffman Coding and Bit Plane
Slicing
o Adaptive Huffman builds frequency table according to data
statistics
o Bit plane slicing highlights contribution of each bit in
appearance of image
o Slice gray scale images into 8 binary images using bit plane
slicing
o Initialize dictionary with 0 & 1
o Each output associates frequency counter to phase in with
binary codes; to decrease number of bits
o Results show improvement depends on type of image;
correlation between intensities

11. o Compression ratio of gray scale images is approx. 102%
over standard LZW algorithm

12. Compression ratio of colored images is approx. 55.6% over
standard LZW algorithm

13. IMPLEMENTATION OF LZW IN MATLAB

14. APPLICATIONS
 Medical science
 ECG [4]
 Used in GIF, TIFF and PDF files
 Used in programs like PKZIP
 Hardware devices

15. REFERENCES
[1] Dheemanth H N ,“LZW Data Compression”, American
Journal of Engineering Research (AJER) e-ISSN : 2320-0847 p-
ISSN : 2320-0936 Volume-03, Issue-02, pp-22-26
[2] Simrandeep kaur, Student and V.Sulochana Verma ,Project
Consultant, “Design and Implementation of LZW Data
Compression”, International Journal of Information Sciences and
Techniques (IJIST) Vol.2, No.4, July 2012
[3] Sawsan A. Abu Taleb, Hossam M.J. Musafa, Asma’a M.
Khtoom, Islah K. Gharaybih, “Improving LZW Image
Compression”, European Journal of Scientific Research ISSN
1450-216X Vol.44 No.3 (2010), pp.502-509
[4] Mridul Kumar Mathur, Dr. Akhil Ranjan Garg , Prof. Mukesh
Upadhayay, “Application of LZW Technique for ECG Data
Compression”, International Journal of Advances in Computer
Networks and its Security.