Image Data Compression Based on Two Hybrids Algorithms

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Adil I. Khalil, et. al.

Abstract

Image compression is a way to reduce the storage space of images. Therefore, image compression process increases the performance of such as the data transmission process.  This paper aims to present a new technique to compress digital image data. In this technique, two-hybrid algorithms were used to compress the image data.  The first system consists of one-dimensional discrete cosine transform (DCT), differential pulse code modulation (DPCM), and Huffman code for difference signal. The second hybrid system utilizes an expert system called Learning Automata (LA) to code the difference signal obtained from the first system. A compression ratio of about (10.8:1) was obtained from the first system. The second system provides a (20.6:1) compression ratio with non-noticeable impairment. The information loss is caused by a hybrid (DCT/DPCM) system, not by the LA system. The conclusion drawn is that using two-hybrid systems to compress the image data provides a high compression ratio. Furthermore, learning automata is preferable since it removes all the redundancy in the row data. However, in learning automata,  a Huffman code is determined pixel by pixel which takes a long time.

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