A Critical Review of Well Known Method For Image Compression

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A Critical Review of Well Known Method For Image Compression
Nidhi A. Sodha1, Hiral R. Shah2,
1,2
Department of Computer Engineering,
Noble engineering college, Junagadh.
Gujarat 362001, India.
ABSTRACT: The increasing attractiveness and trust in a digital photography will rise its use for
visual communication. But it requires storage of large quantities of data. For that Image compression
is a key technology in transmission and storage of digital images. Compression of an image is
significantly different then compression of binary raw data. Many techniques are available for
compression of the images. But in some cases these techniques will reduce the quality and originality
of image. For this purpose there are basically two types are introduced namely lossless and lossy
image compression techniques. This paper gives intro to various compression techniques which is
applicable to various fields of image processing.
Keywords: Compression; Image Compression; Lossy Compression; Lossless Compression;
Encoding; Decoding; Redundancy
I. INTRODUCTION
Images are important documents now days. Work with images in some applications we need to do
compression depends on our aim of the applications such as storage of images in a data base, picture
archiving, TV and videoconferencing. Image compression plays a important role in the transmission
and storage of image data because of storage limitations. The main aim of image compression is to
represent an image in the less number of bits without losing the essential information content from
original image.
Many algorithms that performs image compression in many different ways from that some are
lossless and some are lossy. Lossless it will keep the same information as in the original image but in
lossy compression we loss some information from original image while compressing an image.
Image compression is nothing but reducing the amount of data required to represent an image. After
performing compression we can calculate the compression ratio which is is defined as follows:
Cr= N1/N2
Where N1 and N2 are data of images respectively original image and compressed image. From this
formula as increasing the compression ratio it will increase the reduction of data. From this equation
as increase the compression rate decrease the data [1].
II. IMAGE COMPRESSION
Image compression is nothing but reducing the amount of data required to represent an image. Image
compression was done by taking advantage of redundant data because every image having some
redundant data redundant data means duplication of data or we can says that in a image some pixels

International Journal of Modern Trends in Engineering and Research (IJMTER)
Volume 01, Issue 05, [November - 2014] e-ISSN: 2349-9745, p-ISSN: 2393-8161
are duplicates or some pattern occurs frequently in an image. So we can achieve image compression
when this redundancies are reduce or eliminated. There are three basic data redundancy use for the
compression that are:
1. Inter Pixel Redundancy
In image pixel are not independent they are correlated to their neighboring pixels in this type of
redundancy there are many ways from that one is predict the value of pixel by the value of their
neighboring pixels.
2. Coding Redundancy
In this variable length code words are use and store in a lookup tables (LUTs).that variable length
code words selected to match statistics of the original image. This technique is always reversible
huffman coding and arithmetic coding are example of this technique.
3. Psycho Visual Redundancy
From many experiments prove that human eye does not respond equally to all visual information.
For images some pieces are more important than others .now a day most of image coding algorithm
using this type of redundancy technique.
The main purpose for using compression algorithm is to represent given data in to law bit rates[2].
There are number of ways to evaluating compression algorithm. and for the measuring an algorithm
we can find complexity of the algorithm, how much memory require to implement an algorithm, on
given machine how fast that algorithm will perform ,compression rate and how closely reconstructed
image resemble the original image. simple block diagram for image compression system is as shown
in fig(1). devise who perform compression task is known as encoder from given diagram encoder
compress image from A to B. Where A is input image and B is compressed image which is then
passes to decoder through channel or a storage system. Then from compressed data decoder will
reconstruct or decompress image C as per our application channel will affected by noice so it will
distort image during the transmission here we assume channel as a error-free channel from given
figure for lossless compression C is identical to A where for lossy compression C is different from
A.
Figure 1. Block diagram of image compression system
2.1. Lossless Compression
As the name itself indicates the original image can be perfectly recovered using the lossless
compression techniques. This technique is also known as entropy coding, noiseless compression etc.
They will not introduce any noises to the image and they are using statistics or decomposition
techniques to reduce the redundancy. These techniques are preferred for medical imaging, technical
drawing etc. The following are some different type of the methods which are used for lossless
compression.

1. Runlength encoding.
2. Entropy encoding.
3. Huffman encoding.
4. Arithmetic encoding.
1) Run Length Encoding:
It is a very simple form of image compression in which runs of data are stored as a single data value
and count, rather than as the original run. It is used for sequential data and it is helpful for repetitive
data. In this technique replaces sequences of identical symbol (pixel), called runs. The Run length
code for a grayscale image is represented by a sequence {Vi, Ri} where Vi is the intensity of pixel
and Ri refers to the number of consecutive pixels with the intensity Vi as shown in the figure. This is
most useful on data that contains many such runs for example, simple graphic images such as icons,
line drawings, and animations. It is not useful with files that don't have many runs as it could greatly
increase the file size. Run-length encoding performs lossless image compression [4]. Run-length
encoding is used in fax machines.
35 35 35 40 40 40 40 70 70 70
2) Entropy Encoding:
In information theory an entropy encoding is a lossless data compression scheme that is independent
of the specific characteristics of the medium. One of the main types of entropy coding creates and
assigns a unique prefix-free code for each unique symbol that occurs in the input. These entropy
encoders then compress the image by replacing each fixed-length input symbol with the
corresponding variable-length prefix free output codeword.
3) Huffman Encoding: In computer science and information theory, Huffman coding is an entropy
encoding algorithm used for lossless data compression. It was developed by Huffman. Huffman
coding [8] today is often used as a "back-end" to some other compression methods. The term refers
to the use of a variable-length code table for encoding a source symbol where the variable-length
code table has been derived in a particular way based on the estimated probability of occurrence for
each possible value of the source symbol. The pixels in the image are treated as symbols. The
symbols which occur more frequently are assigned a smaller number of bits, while the symbols that
occur less frequently are assigned a relatively larger number of bits. Huffman code is a prefix code.
This means that the (binary) code of any symbol is not the prefix of the code of any other symbol.
4) Arithmetic Coding:
Arithmetic coding is a form of entropy encoding used in lossless data compression. Normally, a
string of characters such as the words "hello there" is represented using a fixed number of bits per
character, as in the ASCII code. When a string is converted to arithmetic encoding, frequently used
characters will be stored with little bits and not-so-frequently occurring characters will be stored with
more bits, resulting in fewer bits used in total. Arithmetic coding differs from other forms of entropy
encoding such as Huffman coding [10] in that rather than separating the input into component
symbols and replacing each with a code, arithmetic coding encodes the entire message into a single
number.
{35,3} {40,4} {70,3}

2.2 LOSSY COMPRESSION TECHNIQUES
Lossy schemes provide much higher compression ratios than lossless schemes. By this scheme, the
decompressed image is not identical to the original image, but reasonably close to it. But this scheme
is widely used. Lossy methods are especially suitable for natural images such as photographs in
applications where minor loss of fidelity is acceptable to achieve a substantial reduction in bit rate.
The lossy compression that produces imperceptible differences may be called visually lossless. The
following methods are used in lossy compression
1. Chroma subsampling
2. Transform coding
3. Fractal Compression
1) Chroma subsampling
This takes advantage of the fact that the human eye perceives spatial changes of brightness more
sharply than those of color, by averaging or dropping some of the chrominance information in the
image. It works by taking advantage of the human visual system's lower acuity for color differences
than for luminance.[1] Ii is mainly used in video encoding, jpeg encoding etc. Chroma sub sampling
is a method that stores color information at lower resolution than intensity information. The
overwhelming majority of graphics programs perform 2x2 chroma sub sampling, which breaks the
image into 2x2 pixel blocks and only stores the average color information for each 2x2 pixel group.
This process introduces two kinds of errors.
2) Transform coding
It is a type of compression for natural data like photographic images. It will result a low quality
output of original image. It is a core technique recommended by jpeg. Transform coding is used to
convert spatial image pixel values to transform coefficient values. Since this is a linear process and
no information is lost, the number of coefficients produced is equal to the number of pixels
transformed. Many types of transforms have been tried for picture coding, including for example
Fourier, Karhonen-Loeve, Walsh-Hadamard, lapped orthogonal, discrete cosine (DCT), and recently,
wavelets.
3) Fractal Compression It is one of the lossy compression technique used in digital images. As the
name indicates it mainly based on the fractals. This approach is good for natural images and textures.
It works on the fact that parts of an image often resemble other parts of the same image. This mehod
converts these parts into mathematical data. These data are called “fractal codes” Which are used to
recreate the encoded image.
V. CONCLUSION
This paper survey different image compression techniques this methods are mainly classified into
two major categories that are lossy compression and lossless compression . As their name it self
suggest how they works. In lossless technique it decode without loss of any information. At other
and in lossy technique there surtain loss of information from data which represent image both
techniques have their own applications like lossy compression used in a multimedia data and lossless
used in a text or data file like bank records, text articles. Sometimes it is helpful to make on master
lossless file which is used to make compressed file for different applications.
.
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A Critical Review of Well Known Method For Image Compression

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