Vector Predictive Coding of Speech at 16 kbits/s
V. Cuperman, A. Gersho · IRE Transactions on Communications Systems · 1985
Vector quantization, in its simplest form, may be regarded as a generalization of PCM (independent quantization of each sample of a waveform) to what might be called "vector PCM," where a block of consecutive samples, a vector, is simultaneously quantized as one unit. In theory, a performance arbitrarily close to the ultimate rate-distortion limit is achievable with waveform vector quantization if the dimension of the vector,k, is large enough. The main obstacle in effectively using vector quantization is complexity. A vector quantizer of dimensionkoperating at a rate ofrbits/sample requires a number of computations on the order ofk2^{kr}and a memory of the same order. However, a low-dimensional vector quantizer (dimensions 4-8) achieves a remarkable improvement over scalar quantization (PCM). Consequently, using the vector quantizer as a building block and imbedding it with other waveform data compression techniques may lead to the development of a new and powerful class of waveform coding systems. This paper proposes and analyzes a waveform coding system, adaptive vector predictive coding (AVPC), in which a low-dimensionality vector quantizer is used in an adaptive predictive coding scheme. In the encoding process, a locally generated prediction of the current input vector is subtracted from the current vector, and the resulting error vector is coded by a vector quantizer. Each frame consisting of many vectors is classified into one ofmstatistical types. This classification determines which one ofmfixed predictors and ofmvector quantizers will be used for encoding the current frame.