We compression error. Our main objective is to

We
will make image/picture compression using efficient fuzzy logic in this
research. We will use quadtree algorithm for this purpose. We opt for fuzzy logic
based method as fuzzy logic is considered strong tool to handle vagueness.
When images are vague in terms of pixel values fuzzy logic is considered
appropriate logic for its analysis. In proposed technique one domain block is
considered for every range block & searched only for matched contrast
scaling. So outcomes fractal code does not contain coordinates of matched
domain block. Quadtree algorithm may be here applied in such case & size
of range block may be minimized as small as 2×2 pixels. proposed research
deals with integration of quad tree algorithm with conventional DCT based
fractal image compression in order to produce higher compression ratio PSNR
with less compression error. Our main objective is to review the image of
high compression and resolution and in order to compress it we use a novice
algorithm and implements it in image so that the pixels of the image got
compressed. In order to achieve this objective we use Fuzzy Logic.

In imaging science, image processing is any form of signal processing for which the
input is an image, such as a photograph or video frame; the output of image
processing may be either an image or a set of characteristics or parameters
related to the image. Most image-processing techniques involve treating the
image as a two-dimensional signal and applying standard signal-processing
techniques to it.
Image processing usually refers to digital image processing,
but optical and analog image processing also are possible. The acquisition of images (producing
the input image in the first place) is referred to as imaging. An image may be considered to contain sub-images
sometimes referred to as regions-of-interest, ROIs, or simply regions. This
concept reflects the fact that images frequently contain collections of
objects each of which can be the basis for a region. In a sophisticated image
processing system it should be possible to apply specific image processing
operations to selected regions. Thus one part of an image (region) might be
processed to suppress motion blur while another part might be processed to
improve color rendition.
Image processing is a method to convert an image into
digital form and perform some operations on it, in order to get an enhanced
image or to extract some useful information from it. It is among rapidly
growing technologies today, with its applications in various aspects of a
business. Image Processing forms core research area within engineering and
computer science disciplines too.
Image
processing basically includes the following three steps:
1. Importing the image with optical
scanner or by digital photography.
2. Analyzing and manipulating the
image which includes data compression and image enhancement and spotting
patterns that are not to human eyes like satellite photographs.
3. Output is the last stage in
which result can be altered image or report that is based on image analysis.
Fundamental
steps in digital image processing are
 
Algorithm:

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In
this implementation first of all the algorithm reads an image and
defines the size of the range blocks & domain blocks.
As
per the defined size  of range
blocks and domain blocks the algorithm breaks the image in respective
horizontal and vertical address of blocks.
The
image blocks of size 16 X 16 are saved as TP. These blocks are further
modified by reducing the pixel values by half.
DCT
is applied on each block and saved as TRR. Same operation is to be
performed on domain blocks and they are saved as TD in a size of 32 X
32.
Then
the DCT of domain blocks are saved as TDM.
Then
the domain blocks are down sampled to the size of 16 X 16. Then the
error between range blocks and domain blocks will be evaluated.
As
per the array values fuzzy logic will decide the no. of fractals prior
to applying encoding the pixels.

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