The compression data problem is one of most important problems
nowadays, because it saves storage requirements, and reduces the time for the processing, The compressed data give the returns of main data in few times.
In this article, we offer an al
gorithm for recognizing the closed and
unclosed shapes, and that was done by compressing the images using haar wavelet, and we noted that the compressed images give the wanted returns in about quarter the time that main images take.
This algorithm was done using the Mathematica 8.0 program as one of the most powerful programming languages.
Image compression is one of the most important branches of digital image
processing. It reduces the size of the captured images and minimizes the storage space on
the drivers to speed up the transferring and transmission.
In this paper we will pre
sent a new approach for compressing stereo images based on
three algorithms; the first one is comparing the two images that perform the stereoscopic
view by noticing the great similarities between them and encoding the difference between
the two images instead of encoding the whole image. The second one is reducing the
redundancy between the Pixels using a 2D Digital Curvelet Transformation so we can
utilize the great ability to represent the curves in the image with minimum number of
coefficients. Then quantize them and remove undesirable coefficient. The low number of
coefficient contains most of image data. Last one is using Huffman Encoding and take
advantage of the lossless property so we can encode image and reduce the size of data
without getting any image distortion or lose any part of this image.
The performance of the proposed algorithm evaluated using Compression Ratio
standard which is the number of the image bits after compression to the number of the
original image bits before compression. Also, Peak Signal to Noise Ratio standard (PSNR)
which represent the similarity between the restored image and the original image. In final,
the Mean Square Error standard (MSE) which represent the error between the restored
image and original image.
In conclusion, the main objective here is to get the lowest rate for image compression
ratio with the highest value for the image quality PSNR at the lowest value of the errors
MSE.
