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Locational Image Compression based on Chain Code Representation

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 توفيق عبد الخالق عباس الاسدي
17/06/2014 06:32:17
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Locational Image Compression based on Chain Code Representation

Prof. Dr. Tawfiq Abdulkhaleq Abbas 1

 Fanar Ali Joda 2 1(Collage of Information Technology – Babylon University - Iraq)

 2(Collage of Information Technology – Babylon University - Iraq)

Abstract: -

 In this research we have proposed a new compression algorithm that used locational compression technique based on Freeman chain code. The technique consists of two parts, the first part is compression algorithm which starts by obtaining the chain code for particular color value then saving location of start point for chain code, color value and chain code in compressed file, the next step is to remove all color values that related to chain code from input image and shrink the input image, the algorithm repeats the previous procedures until there will be no color values with significant chain code. The second part is to construct the original image by using start point, color value and chain code.

 Keywords: - Chain code, Compressed, Decompressed, Lossless image compression.


Data compression defined as the process of converting an input data stream (the source stream or the original raw data) into another data stream (the output, or the compressed, stream) that has a smaller size. [1] There are many methods for data compression. They are based on different ideas that are suitable for different types of data, such as (text, images, and sound) and produce different results. [2] The image compression is done by removing all redundancy that may exist in image data file so that it takes up less storage space and requires less bandwidth to be transmitted. [3] The data redundancies comprise of three basic redundancies: coding redundancy, inter-pixel redundancy, and psycho-visual redundancy, Coding redundancy some gray levels are more common than others, Inter-pixel redundancy the same gray level covers large areas , Psycho-visual redundancy some color differences are imperceptible. [4] Two types of data compression algorithms can be explained: lossless and lossy. Lossy technique causes image quality degradation in each compression/decompression stage. Careful consideration of the human visual perception guarantees that the degradation is often unrecognizable, though this depends on the selected compression ratio. In general, lossy techniques offer far greater compression ratios than lossless techniques.[5] There are methods of lossy image compression like vector quantization (VQ), JPEG, subband coding, fractal based coding and etc.

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