An associatively classified partitioned vector quantizer

Vector quantization is an effective means of data compression which maps an ordered set of real numbers into a single integer. However, the unconstrained use of vector quantizer (VQ) for accurately compressing a high-dimension signal vector requires high computational complexity and large storage requirement. In order to avoid this problem, structures with some constraints are often used to involve more than one codebook in representing an input vector jointly. In this paper, a novel associatively classified partitioned VQ (ACPVQ) is presented as an attempt to exploit, in a simple way, both the intra and inter subset correlations among the original signal vector components. By the adoption of a modified M-L tree search algorithm plus a novel design procedure of joint partition-and-centroid over different sub-spaces, the novel ACPVQ achieves an average spectral distortion value below 1 dB at 21 bits/frame in quantizing line spectral frequencies (LSFs) which are widely used to represent the spectral envelope information of speech. Compared with the conventional partitioned VQ (Paliwal and Atal, 1993) and enhanced multistage VQ (LeBlanc et al., 1993), which can quantize LSFs with average spectral distortion values below 1 dB at 24 and 22 bits/frame, respectively, the new ACPVQ is obviously more efficient. However, the reduction in bit rate is accompanied by increases in both computational complexity and storage memory