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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  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.
Figure 1. Image compression
A simple and effective source code for AMBTC Image Compression.
Demo code (protected P-files) available for performance evaluation. Matlab Image Processing Toolbox is required.
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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.