ABSTRACT
The
objective of this project is to improve JPEG Compression used for gray
scale images that contains several of objects. The requested improvement
is size reduction of the image file, while maintaining the quality of the
image.
JPEG
Compression is mostly suitable for ‘smooth’ images with a small difference
between neighbor pixels. It is also known that in JPEG Compression part
of the information in the image gets lost and the quality of the image
is damaged. Therefore the possibility of pre-processing the image was examined.
Using this pre-processing, the image was separated to 2 images:
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An image that holds the objects in
the original image. Each object is represented by one gray level that is
the average of the object’s gray levels.
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A smooth image that was accepted after
subtracting the objects’ image from the original picture. This image is
visually meaningless but it enables efficient and qualitative JPEG Compression.
The
first image is compressed in GIF format that is suitable for compression
of images with several gray levels. The second image will be compressed
in JPEG format.
In
order to build the objects’ image, morphological operations were performed
on the original image. These operations sharpen the edges of the objects
in the image. After that there was labeling of the image after the morphological
operations, using REGION GROWING algorithm.
After
decompressing the 2 images and adding the matrices to one image, a comparison
was made between the original image and the new image. The following parameters
were checked:
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The file size.
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The image quality.
The
goal is to compress the image as maximum as possible, meaning that the
sum of the images files’ sizes is as small as can be in reference to the
JPEG Compression of the original image, while maintaining high quality
of the image.
The
algorithm is implemented in MATLAB.
The
algorithm receives as input a gray scale picture or RGB picture, which
is converted to gray scale picture.
The
picture is represented as a matrix.
Using
morphologic operations (first erosion and then dilation, with cross element)
the elements in the picture are clearer and easier to be recognized.
The
recognition of the elements, using raster algorithm, begins at the left
corner of the image and continues to the right, through the columns and
down the rows. Each pixel is compared to its left pixel. If there is a
difference, the pixel is compared to the upper pixel. If the pixel’s value
is similar or ‘close’ to one of his neighbors (left or up), then the new
pixel is part of an element that was already identified. Otherwise the
pixel is apart of a new element. In a new matrix, each element is represented
by a different value. At the end of this process, the elements that were
recognized will appear in this matrix.
Using
this matrix the average value of each element is calculated from the real
values of the pixels in the original picture. If there are several elements
with ‘close’ average values, they are united and the average value is calculated
again. Now the matrix is ready to be compressed as a gif picture.
Subtracting
the gif matrix from the original picture creates the jpeg matrix that will
be compressed as a jpeg picture.
The
two matrices are compressed and examined by a few factors in order to determine
whether the algorithm succeeded.
The
checked parameters are:
-
The size of files after the compression
– this is the basic standard for the algorithm success, and the other parameters
show the efficiency of the algorithm.
-
The MSE (mean square error) and the
PSNR of the pictures - Those parameters give a numeric comparison of jpeg
compression of the original picture versus the new suggested algorithm.
-
Visual test - after compressing the
gif and jpeg pictures, they are decompressed and added. Then the resemblance
to the original picture is examined visually.
Results
As
mentioned before three parameters were examined:
Visual
results
Pictures
in which the objects had sharp and recognizable edges, and a big difference
between the objects’ gray levels returned a final
picture
that is identical to the original one.
MSE
and PSNR
The
computed values of those parameters are considered good and were located
in the upper level of the MSE and PSNR scale.
File
size
After
compression all the pictures were in a smaller size in compare to their
original size, meaning that the total size of the GIF and JPEG pictures
was smaller than the size of the original picture. The total size was about
2/3 of the file size of the original picture.
Conclusions
The
algorithm was found efficient for pictures with clear and sharp objects,
meaning that each pixel can be easily related to it’s own object.
The
definition of the project is dealing with such pictures and therefore the
algorithm is successful. In all the pictures there was an improvement in
the size of the picture, a reduction in the size of the file. The difference
between the checked pictures was in the quality of the final picture. The
JPEG Compression was made in different levels of compression and thus different
qualities.
As
for the quality of the decompressed picture, it was found out that the
main factor is the difference between an object gray scale values (of its
pixels) and the background or another object gray scale value.
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The
original image
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The
image after Morphology
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The
CLUSTER matrix
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a
separated object
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a
separated object
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a
separated object
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The
image to be compressed as GIF
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The
decompressed image
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