Abstract
A Low-complexity and Low-memory Entropy Coder (LLEC) for image compression is proposed in this paper. The two key elements in LLEC are zerotree coding and Golomb-Rice codes. Zerotree coding exploits the zerotree structure of transformed coefficients for higher compression efficiency. Golomb-Rice codes are used to code the remaining coefficients in a VLC/VLI manner for low complexity and low memory. The experimental results show that the compression efficiency of DCT- and DWT-based LLEC outperforms baseline JPEG and EZW at the given bit rates, respectively. When compared with SPIHT, LLEC is inferior by 0.3 dB on average for the tested images but superior in terms of computational complexity and memory requirement. In addition, LLEC has other desirable features such as parallel processing support, ROI (Region Of Interest) coding and as a universal entropy coder for DCT and DWT.
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