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The amount of image data grows day by day. Large storage
and bandwidth are needed to store and transmit the
images, which is quite costly. Hence methods to compress
the image data are essentially now-a-days. The image
compression techniques are categorized into two main
classifications namely Lossy compression techniques and
Lossless compression techniques. Lossless compression
ratio gives good quality of compressed images, but yields only
less compression whereas the lossy compression techniques lead to loss of data with higher compression ratio. JPEG and Block Truncation Coding [3] is a lossy image
compression techniques. It is a simple technique which
involves less computational complexity. BTC is a recent
technique used for compression of monochrome image data. It
is one-bit adaptive moment-preserving quantizer that
preserves certain statistical moments of small blocks of the
input image in the quantized output. The original algorithm of
BTC preserves the standard mean and the standard deviation. The statistical overheads Mean and the Standard deviation
are to be coded as part of the block. The truncated block of the
BTC is the one-bit output of the quantizer for every pixel in
the block .Various methods have been proposed during last
twenty years for image compression such BTC and Absolute
Moment Block Truncation Coding AMBTC. AMBTC
preserves the higher mean and lower mean of the blocks and
use this quantity to quantize output. AMBTC provides better image quality than image compression using BTC.
We have developed a low complex image compression algorithm. The proposed algorithm is a combination of pattern squeezing, moments re-quantizing, absolute moments block truncation coding (AMBTC)
and a postprocessing unit. One advantage of the proposed algorithm is that it reduces and controls the higher bit rate of the AMBTC while preserving a reasonable image quality.
Index Terms: Matlab, source, code, AMBCT, block, truncation, coding, image, compression.
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Figure 1. Image compression |
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A simple and effective source code for AMBTC Image Compression. |
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Demo code (protected
P-files) available for performance evaluation. Matlab Image Processing Toolbox is required. |
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Release |
Date |
Major features |
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1.0 |
2012.04.07 |
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We recommend to check the secure connection to PayPal, in order to avoid any fraud. This donation has to be considered an encouragement to improve the code itself. |
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AMBTC Image Compression - Click here for
your donation. In order to obtain the source code you
have to pay a little sum of money: 40 EUROS (less
than 56 U.S. Dollars). |
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Once you have done this, please email us luigi.rosa@tiscali.it As soon as possible (in a few days) you will receive our new release of AMBTC Image Compression. Alternatively, you can bestow using our banking coordinates:
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The authors have no relationship or partnership
with The Mathworks. All the code provided is written in Matlab
language (M-files and/or M-functions), with no dll or other
protected parts of code (P-files or executables). The code was
developed with Matlab 2006a. Matlab Image Processing Toolbox is required.
The code provided has to be considered "as is" and it is without any kind of warranty. The
authors deny any kind of warranty concerning the code as well
as any kind of responsibility for problems and damages which may
be caused by the use of the code itself including all parts of
the source code.
