IEEE Trans Image Process. 2016 Feb;25(2):713-25. doi: 10.1109/TIP.2015.2509253.
This paper constructs a set partition coding system (SPACS) to combine the advantages of different types of set partition coding algorithms. General tree (GT) is an important conception introduced in this paper, which can represent tree set and square set simultaneously. With the help of GT, SPIHT is generalized to construct degree- k SPIHT based on the analysis of two kinds of set partition operations. Using the same coding mechanism, SPACS (k,p) is constructed, aided with virtual subbands that are generated by recursive division on the LL band. SPACS belongs to tree-set partition coding algorithms if k and p take smaller values. In particular, SPACS(2,1) is the classical SPIHT. SPACS tends toward a block-set partition coding algorithm as k,p increases. Location bit, amplitude bit, and unnecessary bit are presented, which can be used to analyze the coding efficiency of SPACS. We compress 256 images with 512×512 using SPACS. The numerical results show SPACS achieves some improvements in coding efficiency over SPIHT, especially at very low bitrate. On average, to code every image, SPACS(3,1) (at an average of 3.93 bpp) needs 7792 more location bits but saves 10 218 unnecessary bits, compared with SPIHT (3.94 bpp).
本文构建了一种集合划分编码系统 (SPACS),以结合不同类型集合划分编码算法的优势。广义树 (GT) 是本文引入的一个重要概念,它可以同时表示树集和方集。借助 GT,SPIHT 被推广为基于两种集合划分操作的分析,构建了度为 k 的 SPIHT。使用相同的编码机制,构建了 SPACS(k,p),借助递归划分 LL 带生成的虚拟子带。如果 k 和 p 取较小的值,SPACS 属于树集划分编码算法。特别地,SPACS(2,1) 是经典的 SPIHT。随着 k 和 p 的增加,SPACS 趋向于块集划分编码算法。提出了位置位、幅度位和不必要位,可以用于分析 SPACS 的编码效率。我们使用 SPACS 对 256 张 512×512 的图像进行压缩。数值结果表明,SPACS 在编码效率上比 SPIHT 有所提高,尤其是在非常低的比特率下。平均而言,为了对每张图像进行编码,SPACS(3,1)(平均 3.93 bpp)需要多 7792 个位置位,但节省了 10218 个不必要位,而 SPIHT(3.94 bpp)则需要。