Abstract
This paper presents a new method of lossless image compression. An image is characterized by homogeneous parts. The bit planes, which are of high weight are characterized by sequences of 0 and 1 are successive encoded with RLE, whereas the other bit planes are encoded by the arithmetic coding (AC) (static or adaptive model). By combining an AC (adaptive or static) with the RLE, a high degree of adaptation and compression efficiency is achieved. The proposed method is compared to both static and adaptive model. Experimental results, based on a set of 12 gray-level images, demonstrate that the proposed scheme gives mean compression ratio that are higher those compared to the conventional arithmetic encoders.
Highlights
IntroductionThe need for data compression is growing day by day because of the fast development of data intensive applications that place a heavy demand on information storage and to meet the high data rate requirements of bandwidth transmission systems such as multimedia [1]
The need for data compression is growing day by day because of the fast development of data intensive applications that place a heavy demand on information storage and to meet the high data rate requirements of bandwidth transmission systems such as multimedia [1].Compression is the coding of the data to minimize their representation by removing the redundancy present in them, keeping only sufficient information that can be effectively used in the decompressing phase to reconstruct the original data
The bit planes, which are of high weight are characterized by sequences of 0 and 1 are successive encoded with RLE, whereas the other bit planes are encoded by the arithmetic coding (AC)
Summary
The need for data compression is growing day by day because of the fast development of data intensive applications that place a heavy demand on information storage and to meet the high data rate requirements of bandwidth transmission systems such as multimedia [1]. AC is the most powerful technique for statiscal lossless encoding that has attracted much attention in the recent years It provides more flexibility and better efficiency than the celebrated Huffman coding does [2]. In [6] Howard et al give a new paradigm of lossless image compression, with four modular components: pixel sequence, prediction, error modeling and coding. In [7], HOWARD et al showed that high-resolution images can be encoded and decoded efficiently in parallel They present an algorithm based on the hierarchical MLP method used either with Huffman coding or with a new variant of AC called quasiarithmetic coding. We propose a new method of lossless image compression based on combining AC with the RLE.
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