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            To transfer the
image in many types are used this such as computer, mobile and internet etc. The
transmission of images in computer, mobile and internet are etc. To store an
image digital data are required in large quantities. To overcome the problem
limited bandwidth, there is need to compress the image before transmission. To
make clear the problem various image compression techniques have been developed
in digital image processing.   This study
presents a survey on   Image Compression

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Compression, image compression, lossy compression, lossless compression.



Digital images become popular for
transferring visual information which is using the images over traditional
camera film images. It produces instant images, which can be viewed as film
processing. But these images are displayed in large size. To overcome the
problem use of image compression technique is used to reduce to size without
affecting the quality of image. The reduction performed to store the more
images in the disk or given memory space. To reduce the size required a
bandwidth less and quickly to transfer the image or less time transfer the
image related to cost.



The set of compression techniques
used in image processing are for various applications. The compression techniques
are classified in two. One is lossless compression and another one is lossy

Lossless Compression

Lossy Compression


              Lossless compression perform in original
image can be perfectly recovered from the compression image which is provide a
quality of image. It is also known as entropy coding since to eliminate the
redundancy use decomposition technique. These are mainly used for application
like medical imaging, technical drawing etc. Some of the methods are using lossless
compression technique.

Ø  Huffman

Ø  Area

Ø  Arithmetic

Ø  Run
Length Coding



             Huffman coding based occurs on the
frequency and probabilities. The frequency occurs on the file. To reduce the
file size by 10% to 50% for Huffman coding and irrelevant information can be

 Each pixel is treated as a symbol. The symbols
are representing a higher frequency which is assigned a smaller number of bits which
the symbol less frequency is assigned a relative large number of bits. Huffman
algorithm used to for application in JPEG. The advantages of easy to implement
and It is an optimal and compact code. The disadvantage is algorithm can relatively
slow. It depends on the statistical model of data. The decoding process
difficult and different code length



       The area coding enhanced from run length
coding of lossless compression technique. It is a highly effective which is
provides a better compression ratio (CR). It reflect the two dimensional
character of image. But it produces some limitation. It cannot implement the
hardware because of non-linear method.  The
advantages of the area coding technique use over lossless other methods. It is
used to special code words which is identifying the large areas of contiguous
0’s and 1’s. The image can be segmented into a blocks. Segments are classified
as block. It only contains a black and white pixels or block with mixed
intensity and all pixels of the block have same value.



           LZW is a
Lemple-Ziv-Welch. LZW based on the dictionary. The dictionary is classified in
two. One is represent a static and another one is represent dynamic. The static
perform a dictionary for fixed encoding. Dynamic perform a dictionary is
updated for decoding process. The applications are used as a TIFF and GIF
files. The advantage of lzw coding is easy to implement and compression perform
is fast. The disadvantages are to make string table is difficult and storage
need an indeterminate.



          Lossy compression
techniques perform a data can be compressed and loss of information. Various lossless compression techniques
higher compression ratio in reconstruction of the image. It provides a quality
of data for better compression. It performs to remove redundancy of the
original images. The following methods are using for lossy technique.

Ø Transform

Ø  Vector

Ø  Wavelet

Ø  Fractal


             Transform coding is one of the Lossy
compression techniques in which the original image can be into small blocks of
smaller size. This technique is used as a data audio signal or biomedical
image. This type of coding required a lesser bandwidth.

Transform coding use DCT (discrete
coding transform) which is perform as used to change the pixel of the original
image. The widely used for the transform coding, JPEG image compression
standard adopted transform coding technique.



Quantization is one of the most lossy compression techniques. VQ is a very
powerful technique for digital image compression. VQ extension of scalar
quantization but with multiple dimensions. VQ need to develop for code vectors which
dictionary performs a fixed-size of vectors. 
Which means image again divided into non-overlapping blocks, this are
knows as image vectors. The dictionary is determined closest matching vector for
each image vector. The original image vector is encoded which is use for the
dictionary. It is widely used as a multimedia application.  The advantage VQ is simple decoder and no
coefficient quantization. The Disadvantages
is generating a slow codebook and Small bpp.



 Wavelet coding is one of the most popular lossy
compression technique, Wavelet means a “smallwave” the waves are implies to a
window function of finite length. Wavelet functions are approach mathematics.
Wavelet Compression algorithm performed Discrete Wavelet Transform (DWT). Such
as Embedded Zero Wavelet (EZT) performance is excellent. The compression quantization
of the image which is specified the wavelet space image of sub-band. Image
compressions do the encoding of sub-band. Inverse or Reverse order successively
perform the image decompression, or reconstruction and which decode, dequantize
and inverse Discrete Wavelet Transformation.   
The advantages is a high compression ratio, State-Of-The- Art, low
encoding complexity and It produce no blocking artifacts. The Disadvantages is Coefficient
Quantization, Bit allocation and less



            It  is 
one  of  the most 
lossy  compression  technique 
used  in  digital 
images. It mainly based on the fractals. This approach natural images,
edge detection, color separation, spectrum and textures analysis. It performs the
fact parts of an image and resembles other parts of the same image. This are
convert parts into mathematical data. These data are called “fractal codes” which
are used to recreate the encoded image. The advantage is a good mathematical
encoding-frame and resolution encoding. The disadvantages is a slow encoding



            Basic  concept 
of  image  compression 
and  various  technologies 
used  are  discussed 
in  this  paper. 
We  have  also 
discussed  advantages  and 
disadvantages  of  some 
lossless image compression   and
lossy image  compression  techniques. A survey is performed on the most
essential and advance compression methods, in lossless technique the image can
be decoded without any loss of information. But in case of lossy compression it
cause some form of information loss. These techniques are good for various
applications. Lossy compression is most commonly used to compress multimedia
data like audio, video, and still images, especially in applications such as
streaming media. By contrast, lossless compression is required for text and data
files, such as bank records and text articles. It produce quality image, amount
of compression and speed of compression.

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