A low memory zerotree coding for arbitrarily shaped objects
The Set Partitioning In Hierarchical Trees (SPIHT) algorithm is a computationally simple and efficient zerotree coding technique for image compression. However, high working memory requirement is its main drawback for hardware realization. In this study, we present a low memory zerotree coder (LMZC), which requires much less working memory than WHIT. The LMZC coding algorithm abandons the use of lists, defines a different tree structure, and merges the sorting pass and the refinement pass together. The main techniques of LMZC are the recursive programming and a top-bit scheme (TBS). In TBS, the top bits of transformed coefficients are used to store the coding status of coefficients instead of the lists used in SPIRT. In order to achieve high coding efficiency, shape-adaptive discrete wavelet transforms are used to transformation arbitrarily shaped objects. A compact emplacement of the transformed coefficients is also proposed to further reduce working memory. The LMZC carefully treats "don't care" nodes in the wavelet tree and does not use bits to code such nodes. Comparison of LMZC with SPHIT shows that for coding a 768 x 512 color image, LMZC saves at least 5.3 MBytes(1) of memory but only increases a little execution time and reduces minor peak signal-to noise ratio (PSNR) values, thereby making it highly promising for some memory limited applications.
low memory image compression recursive programming arbitrarily shaped image coding shape adaptive zerotree coding