Showing papers on "Codebook published in 1982"

Image coding using vector quantization

Abstract: An image is partitioned into cells of pxp pixels. Each cell is regarded as a vector of dimension p2and is encoded by searching through a codebook for a nearest matching representative vector. A binary word identifying the selected representative vector is assigned as the codeword to describe the original cell. The decoder uses this codeword to address a codebook. Each entry of the codebook contains a full precision digital representation of one of the N representative vectors. The codebook design is based on a clustering technique for vector quantizer design preceded by a classification of training cells into edge or shade cells. Results for coding rates from 0.5 to 1.5 bits/pixel are discussed. Vector quantization appears to be a powerful and promising technique for image coding.

Vector Quantizers and Predictive Quantizers for Gauss-Markov Sources

Abstract: Low-rate vector quantizers are designed and simulated for highly correlated Gauss-Markov sources and the resulting performance is compared with Arnstein's optimized predictive quantizer and with Huang and Schultheiss' optimized transform coder. Two implementations of vector quantizers are considered: full search vector quantizers-which are optimal but require large codebook searches-and tree searched vector quantizers-which are suboptimal but require far less searching. The various systems are compared on the basis of performance, complexity, and generality of design techniques.

Multipath Search Coding of Stationary Signals with Applications to Speech

Abstract: This paper deals with the application of multipath search coding (MSC) concepts to the coding of stationary memoryless and correlated sources and of speech signals at a rate of one bit per sample. We have made use of three MSC classes: 1) codebook coding (vector quantization), 2) tree coding, and 3) trellis coding. This paper explains the performances of these coders and compares them both with those of conventional coders and with rate-distortion bounds. Figs. 2 and 3 demonstrate the potentials of MSC coding strategies. The paper reports also on results of MSC coding of speech, where both the strategy of adaptive quantization and of adaptive prediction were included in coder design.

A Multirate Voice Digitizer Based Upon Vector Quantization

Abstract: The importance of integrating voice and data over digital networks has increased during the last few years primarily because of the growing popularity of such networks. Of particular interest are efficient voice digitizing terminals, capable of operating at various data rates in both circuit-switched and packet-switched data networks. Several such terminals, including two or more speech compression algorithms, have been proposed and implemented. Typically the terminal switches between a low-rate (500 - 4000 bits/s) vocoding scheme and a medium-rate (7000 - 16000 bits/s) waveform coding algorithm, depending on, among other things, the network congestion and on the desired voice quality and robustness. We here describe the design and simulation of a multirate voice digitizer (MRVD) that switches between two speech compression systems, each based on a recently developed vector quantization (VQ) coding technique. This technique consists of the off-line interactive design of a codebook minimizing an average distortion measure, followed by the use of the codebook in an on-line nearest neighbor encoding scheme. One of the two systems is a rate-distortion speech coder that resembles a linear predictive coding (LPC) speech compression system but has a much lower rate (800 bits/s and below). We call this the LPC-VQ system, and it is similar to other previously reported systems [15],[19],[21]. The only difference is that the LPC parameters are extracted using the Burg method instead of the autocorrelation method. We here show that this provides both qualitative and quantitative improvements. The other system of our MRVD is a residual-excited linear predictive (RELP) speech compression system using VQ in both model selection and residual digitization. The residual waveform is digitized at 1 or 2 bits/sample, resulting in rates of 7300 and 13800 bits/s, respectively. We call this the RELP-VQ system. When compared to other RELP systems [6]-[8], it is shown to have a simpler architecture and to provide comparable speech quality. In a direct comparison with an APC scheme, our RELP-VQ system was determined to provide a more natural speech sound. Another interesting result presented is the quantitative comparison of the application of the VQ algorithm to the original speech waveform and its residuals.

Discrete utterance speech recognition without time normalization

Abstract: We present a new, fast method for discrete utterance recognition of telephone bandwidth speech. The method is based on speech coding by vector quantization and minimum cross-entropy pattern classification. Separate vector quantization codebooks are designed from training sequences for each word in the recognition vocabulary. Inputs from outside the training sequence are classified by performing vector quantization and finding the codebook that achieves the lowest average distortion per speech frame. The new method obviates time normalization and uses approximately 6000 bits to represent each utterance in the recognition vocabulary. Preliminary limited testing on speaker dependent digit recognition has demonstrated excellent performance. Detailed tests are now in progress.

Voice Coding and Tree Encoding Speech Compression Systems Based Upon Inverse Filter Matching

Abstract: Two speech compression systems based on codebooks of inverse filters produced by off-line linear predictive coding (LPC) and vector quantization (VQ) techniques are considered. The first system is a pitch excited vocoder that is a variation on a speech coding system based upon vector quantization. The encoder selects an LPC reverse filter from a finite codebook that best "matches" an observed frame of sampled speech. This filter is in turn used to determine the voicing and digitized pitch information. Unlike LPC systems, the digitization is performed in a single step on the data rather than separate modeling and digitization steps. The second system is a tree encoding system that uses the filter selected by an inverse filter matching vocoder to "color" a tree that is then searched for a minimum distortion path for the original sampled speech waveform. This system can be viewed as a hybrid between an adaptive predictive coder and a universal tree encoder. The two systems are described, simulated, and compared with other similar systems.