This document compares lossy and lossless image compression using various algorithms. It discusses the need for image compression to reduce file sizes for storage and transmission. Lossy compression provides higher compression ratios but some loss of information, while lossless compression retains all information without loss. The document proposes comparing algorithms like Fractal image compression and LZW, analyzing parameters like SNR, PSNR, and MSE for formats like BMP, TIFF, PNG and JPEG. It provides details on how the LZW and Fractal compression algorithms work.

1. COMPARISON OF LOSSY AND LOSSLESS
IMAGE COMPRESSION USING VARIOUS
ALGORITHM
E.CINTHURIYA -ME
828106403001

2. IMAGE COMPRESSION
• Image compression is minimizing the size in bytes of a
graphics file without degrading the quality of the image to an
unacceptable level .
• The reduction in file size allows more images to be stored in
a given amount of disk or memory space. It also reduces the
time required for images to be sent over the Internet or
downloaded from Web pages.
• Image Compression is used in the field of Broadcast TV,
Remote sensing , Medical Images.
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3. IMAGE COMPRESSION
Image encoder
Original image
262144 bytes
Compressed bit stream
00111000001001101…
(2428 Bytes)
Image
decoder
Compression ratio (CR) = 108:1
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4. NEED OF IMAGE COMPRESSION
 Image compression techniques are of prime importance for
reducing the amount of information needed for the picture
without losing much of its quality.
 To reduce the size of stored
 Transmitted files to manageable sizes
 To reduce the time it would take to transmit these files to
another computer.
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5. TYPES IMAGE COMPRESSION
Image compression can be performed by two
ways:-
Lossy Compression
Lossless Compression
Lossless Compression the data is compressed
without any loss of data.
Lossy Compression it is assumed that some loss
of information is acceptable. Is suitable for
natural image.
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6. HOW TO ACHIEVE COMPRESSION?
• Minimizing the redundancy in the image.
Redundancy
Interpixel psycho visual coding
Redundancy Redundancy Redundancy
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7. IMAGE COMPRESSION SCHEM
Image compression schem
Pixel Prediction Transform Hybrid
Run length DPCM DC JPEG
Huffman ADPCM DWT JPEG 2000
DM
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8. LOSSLESS COMPRESSION
 With lossless compression, data is compressed without any loss of
data.
 It assumes you want to get everything back that you put in i.e., we
can reconstruct a perfect reproduction of the original from the
compression.
 Lossless compression ratios usually only achieve a 2:1 compression
ratio.
 Useful for text, numerical data, use of scanners to locate details in
images, etc. where there is a precise meaning for the data.
 Even for images or other perceived signals, lossless compression is
sometimes required, particularly for legal documents, medical
images,
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9. LOSSY COMPRESSION
With lossy compression, it is assumed that some loss of information
is acceptable.
When we reconstruct the information from the compressed data,
we get something close to but not exactly the same as the
original.
Lossy compression can provide compression ratios of 100:1 to 200:1,
depending on the type of information being compressed
Lossy compression techniques are often "tunable" in that you can
turn the compression up to improve throughput, but at a loss
in quality.
Lossy compression is very useful for images, audio signals, or
other information that is perceived through our senses.
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10. DIFFERENCE BETWEEN LOSSLESS &
LOSSY IMAGES
Lossless image Lossy image
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11. FORMAT NAME CHARACTERISTICS
BMP Windows bitmap Lossy : Uncompressed format
TIFF Tagged Image
File Format
Lossless: Document scanning and
imaging format. Flexible: LZW, CCITT,
RLE.
PNG Portable Network
Graphics
Lossless: Improve And Replace Gif,
Superior To Tiff
JPEG Joint
Photographic
Experts Group
Lossy : Big Compression Ratio, Good
For Photographic Images
JPEG 2000 Joint
Photographic
Experts Group
2000
Lossy : Eventual replacement for
JPEG
FIVE DIFFERENT FORMATS
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12. PARAMETERS FOR COMPARISON
• COMPRESSION RATIO
The compression ratio is given by:
Size of original image data
Size of compressed image data
CR =
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13. PARAMETERS FOR COMPARISON
• MSE:
Mean square error is defined as the measure of average of
square of ratio of estimator output to the estimated output. it is
also known as the rate of distortion in the retrieved image.
MSE is the power of the corrupted noise signal.
Mean square error is given in decibels by
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14. PARAMETERS FOR COMPARISON
• SNR:
The standardized quantity of measuring the image quality is
the signal-to-noise ratio. It is given by ratio of the power of
the signal to the power of noise in the signal.
SNR is given in decibels by
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15. PARAMETERS FOR COMPARISON
• PSNR:
The most common case of representing the picture of the
input image is given by the Peak value of SNR.
It is defined as the ratio of the maximum power of the signal
to the power of the corrupted noise signal.
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16. PROPOSING SYSTEM
Title : comparison of lossy and lossless image
compression using various algorithm
Algorithm : Fractal image compression algorithm and
LZW
Format : BMP , TIFF - lossless image compression
PNG , JPEG - lossy image compression
Parameters SNR , PSNR , MSE , CR
Compared :
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17. LWZ ALGORITHM
 LWZ is Dictionary-based Coding algorithm .
 The LZW algorithm is named after the scientists Lempel, Ziv
and Welch. It is a simple dictionary based algorithm used for
the lossless compression of images.
 LZW uses fixed-length code words to represent variable-
length strings of symbols/characters that commonly occur
together, e.g., words in English text.
 The LZW encoder and decoder build up the same dictionary
dynamically while receiving the data.
 LZW places longer and longer repeated entries into a
dictionary, and then emits the code for an element, rather
than the string itself, if the element has already been placed
in the dictionary.
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18. Example 1: Compression using LZW
Encode the string BABAABAAA by the LZW encoding algorithm.
1. BA is not in the Dictionary; insert BA, output the code for its prefix: code(B)
2. AB is not in the Dictionary; insert AB, output the code for its prefix: code(A)
3. BA is in the Dictionary.
BAA is not in Dictionary; insert BAA, output the code for its prefix: code(BA)
4. AB is in the Dictionary.
ABA is not in the Dictionary; insert ABA, output the code for its prefix: code(AB)
5. AA is not in the Dictionary; insert AA, output the code for its prefix: code(A)
6. AA is in the Dictionary and it is the last pattern; output its code: code(AA)
The compressed message is: <66><65><256><257><65><260> 18/28comparison of lossy and lossless
compression

19. MERITS OF LWZ
• LZW algorithm is capable of
producing compressed images without having
an effect on the quality of the image.
• It computationally fast algorithm
and is very effective, since the decompression
does not need the strings to be passed to the
table
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20. FRACTAL IMAGE COMPRESSION
• The Fractal image compression is given by Integrated Function
System (IFS).
• In this method it has a source image and the designation image.
The source image is known as the attractor. The designation
image is the output or the recreated image.
• At first the image is partitioned into small parts which are known
as blocks. Those subdivided blocks should not overlap with other
blocks. Each destination block is to be mapped with other block
which is assembled after the removal of repeated bits.
• This has the basic approaches needed to compress the image
known as contacting transformation.
• Then by dividing and contacting the image by a transformation it
is named as fractal transformation or fractal decomposition
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21. FRACTAL IMAGE COMPRESSION
Let us start by scanning every point in the rectangular plane
Each point represents a Complex number (x + iY). Iterate that
complex number:-
[new value] = [old-value]^2 + [original-value]
While keep tracking of two things:
1). The number of iterations
2). The distance of [new-value] from Origin.
If you reach the max. number of iterations, then you are
done with iterations.
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22. FRACTAL IMAGE COMPRESSION
In the diagram above, the functions are represented by their effect on a
square (each function transforms the outlined square into the shaded
square). Both functions are applied to the input image and a union of
the resulting images is formed in each iteration. First three iterations are
shown, and then the final image (fixed point) after several iterations
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23. MERITS OF FRACTAL IMAGE
COMPRESSING
• the image in a contractive form. Fractal
compression is a recent method on lossy
compression based on the use of fractals
which degrades the likeliness of different parts
of an image.
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24. ADVANTAGES OF IMAGE
COMPRESSION
 Less disk space (more data in reality).
 Faster writing and reading.
 Faster file transfer.
 Variable dynamic range.
 Byte order independent.
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25. DISADVANTAGES OF IMAGE
COMPRESSION
 Added complication.
 Effect of errors in transmission.
 Slower for sophisticated methods (but simple
methods can be faster for writing to disk).
 Need to decompress all previous data.
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26. REFERENCES
[1] Lossy and lossless compression using combinational methods
Ms. C.S Sree Thayanandeswari,M.E, MISTE, Assistant Professor,
Department of ECE, PET Engineering College, Vallioor.
[2] Lossless Image Compression Techniques Comparative Study
Walaa Z. Wahba1, Ashraf Y. A. Maghari
[3] 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.
[4] Image Compression- Surovit Roy, Rahul Virmani, Honey Soni,
Prof. Sachin Sonawane
[5] google search and wikipedia search . 26
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