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COMPARISON OF LOSSY AND LOSSLESS IMAGE COMPRESSION USING VARIOUS ALGORITHM
E.CINTHURIYA -ME828106403001
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.
2/28comparison of lossy and lossless compression
IMAGE COMPRESSION
Image encoder
Original image262144 bytes
Compressed bit stream00111000001001101…(2428 Bytes)
Imagedecoder
Compression ratio (CR) = 108:1 3/28comparison of lossy and lossless compression
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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TYPES IMAGE COMPRESSION
Image compression can be performed by two ways:-Lossy CompressionLossless 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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HOW TO ACHIEVE COMPRESSION?
• Minimizing the redundancy in the image.
Redundancy
Interpixel psycho visual codingRedundancy Redundancy Redundancy
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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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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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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 compressedLossy 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, orother information that is perceived through our senses.
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DIFFERENCE BETWEEN LOSSLESS & LOSSY IMAGES
Lossless image Lossy image
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comparison of lossy and lossless compression
FORMAT NAME CHARACTERISTICSBMP 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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PARAMETERS FOR COMPARISON
• COMPRESSION RATIO
The compression ratio is given by:
Size of original image dataSize of compressed image dataCR =
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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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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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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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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 , CRCompared :
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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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comparison of lossy and lossless compression
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> 18/28
MERITS OF LWZ
• LZW algorithm is capable ofproducing compressed images without having an effect on the quality of the image.
• It computationally fast algorithmand is very effective, since the decompression does not need the strings to be passed to the table
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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 20/28comparison of lossy and lossless compression
comparison of lossy and lossless compression
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 iterations2). 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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comparison of lossy and lossless compression
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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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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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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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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comparison of lossy and lossless compression
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REFERENCES[1] Lossy and lossless compression using combinational methodsMs. C.S Sree Thayanandeswari,M.E, MISTE, Assistant Professor, Department of ECE, PET Engineering College, Vallioor.
[2] Lossless Image Compression Techniques Comparative StudyWalaa Z. Wahba1, Ashraf Y. A. Maghari
[3] A. Kumar and A. Makur, “Lossy compression of encrypted imageby 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 .
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comparison of lossy and lossless compression
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