Vector quantization (VQ) is a famous signal compression method. In. a framework of VQ encoding, the fast search method for finding the best-matched codeword (winner) is a key issue because it is the time bottleneck for practical applications. To speed up VQ encoding process, some fast search methods that are based on the concept of multi-resolutions by introducing a pyramid data structure have already been proposed in previous works. However, there still exist two serious problems in them. First, they need a lot of extra memories for storing all purposely-constructed intermediate levels in a pyramid, which becomes an overhead of memory. Second, they completely discard the obtained Euclidean distance that has already been computed at an intermediate level whenever a rejection test fails at this level during a search process, which becomes an overhead of computation. In order to solve the overhead problems of both memory and computation as described above, this paper proposes a memory-efficient storing way for a vector and a recursive computation way for Euclidean distance level by level based on a 2-pixel-merging (2-PM) sum pyramid, which can thoroughly reuse the obtained value of Euclidean distance at any level to compute the next rejection test condition at a successive level. Mathematically, this method does not need any extra memories at all and can reduce the original computational burden that is needed in a conventional non-recursive computation way to about half at each level. Experimental results confirmed that the proposed method outperforms the previous works obviously.
|Journal||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Publication status||Published - 2004|
|Event||Proceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing - Montreal, Que, Canada|
Duration: 2004 May 17 → 2004 May 21
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering