Low-complexity and low-memory entropy coder for image compression

In this paper, a low-complexity and low-memory entropy coder (LLEC) is prop osed for image compression. The two key elements in the LLEC are zerotree c oding and Golomb-Rice (G-R) codes. Zerotree coding exploits the zerotree st ructure of transformed coefficients for higher compression efficiency. G-R codes are used to code the remaining coefficients in a variable-length code s/variable-length integer manner resulting in JPEG similar computational co mplexity. The proposed LLEC does not use any Huffman table, significant/ins ignificant list, or arithmetic coding, and therefore its memory requirement is minimized with respect to any known image entropy coder. In terms of co mpression efficiency, the experimental results show that discrete cosine tr ansform (DCT)- and discrete wavelet transform (DWT)-based LLEC outperforms baseline JPEG and embedded zerotree wavelet coding (EZW) at the given bit r ates, respectively. For example, LLEC outperforms baseline JPEG by an avera ge of 2.2 dB on the Barbara image and is superior to EZW by an average of 0 .2 dB on the Lena image. When compared with set partition in hierarchical t rees, LLEC is inferior by 0.3 dB, on average, for both Lena and Barbara. In addition, LLEC has other desirable features, such as parallel processing s upport, region of interest coding, and as a universal entropy coder for DCT and DWT.