This paper presents an architecture suitable for real-time image coding using adaptive vector quantization. This architecture is based on the concept of content-addressable memory (CAM) where the data is accessed simultaneously and in parallel on the basis of its content. Vector quantization (VQ) essentially involves, for each input vector, a search operation to obtain the best match codeword. Traditionally, the search mechanism is implemented sequentially: each vector is compared with the codewords one at a time. For K input vectors of dimension L and a codebook of size N the search complexity is 0(KLN); this is heavily computation-intensive and therefore real-time implementation of the VQ algorithm is difficult. The architectures reported thus far employ parallelism in the directions of vector dimension L and codebook size N. However, as K > N for image coding, a greater degree of parallelism can be obtained by employing parallelism in the directions of L and K. Matching must therefore be performed from the perspective of the codewords: for a given codeword, all input vectors are evaluated in parallel. A speedup of order SP(KL) results if a CAM-based implementation is employed. This speedup, coupled with the gains in execution time for the basic distortion operation, implies that even codebook generation is possible in real time (<33 ms). In using the CAM, the conventional MSE measure is replaced by the absolute difference measure. This measure results in little degradation and in fact limits large errors. The regular and iterable architecture is particularly well suited for VLSI implementation.
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering