Variable Dimension Vector Quantization Research Articles

Low Bit-Rate Speech Codec Based on a Long-Term Harmonic Plus Noise Model

The long-term harmonic plus noise model (LT-HNM) for speech shows an interesting data compression, since it exploits the smooth evolution of the time trajectories of the short-term harmonic plus noise model parameters, by applying a discrete cosine model (DCM). In this paper, we extend the LT-HNM to a complete low bit-rate speech coder. A Normalized Split Vector Quantization (NSVQ) is proposed to quantize the variable dimension LT-DCM vectors. The NSVQ is designed according to the properties of the DCM vectors obtained from a standard speech database. The obtained LT-HNM coder reaches an average bit-rate of 2.7kbps for wideband speech. The proposed coder is evaluated in terms of modeling and coding errors, bit-rate, listening quality and intelligibility. Index Terms Low bit-rate, speech coding, long term modeling, harmonic plus noise model, variable dimension vector quantization.

Vector Quantization of Harmonic Magnitudes in Speech Coding Applications—A Survey and New Technique

A harmonic coder extracts the harmonic components of a signal and represents them efficiently using a few parameters. The principles of harmonic coding have become quite successful and several standardized speech and audio coders are based on it. One of the key issues in harmonic coder design is in the quantization of harmonic magnitudes, where many propositions have appeared in the literature. The objective of this paper is to provide a survey of the various techniques that have appeared in the literature for vector quantization of harmonic magnitudes, with emphasis on those adopted by the major speech coding standards; these include constant magnitude approximation, partial quantization, dimension conversion, and variable-dimension vector quantization (VDVQ). In addition, a refined VDVQ technique is proposed where experimental data are provided to demonstrate its effectiveness.

Variable dimension vector quantization based image watermarking

In this paper, we present a watermarking method based on variable dimension vector quantization for hiding information in images. Watermark bits are embedded in the dimension information of the variable dimension reconstruction blocks of the cover or input image. Watermark extraction does not require the existence of the original image for oblivious watermarking, while a variation of the scheme for cover escrow watermarking is also presented to increase robustness. Experimental results show the effectiveness of the proposed scheme and this scheme gives comparable capacity with the existing schemes.

Coding of variable dimension speech spectral vectors using weighted nonsquare transform vector quantization

This paper addresses the problem associated with variable-dimension vector quantization, and presents a new quantization technique that combines a variable-size nonsquare transform (NST) with a fixed-dimension vector quantizer. We show that all linear dimension conversion methods can be treated as special cases of a general approach for linear dimension conversion formulated as NST. By incorporating the speech perceptual properties, we introduce a technique called weighted nonsquare transform vector quantization (WNSTVQ) for the quantization of speech spectral vectors. We show that the total perceptual weighted distortion can be separated into the weighted modeling distortion, which is solely determined by the choice of transforms in WNSTVQ and the weighted quantizer distortion. We discuss the factors that influence the performance of the WNSTVQ system and provide a complexity analysis for two WNSTVQ implementations. Finally, experimental results are presented to show that the WNSTVQ system has the ability to trade performance for computational complexity and memory storage by selecting suitable transforms and/or the length of fixed-dimension vectors.

Variable-dimension vector quantization

In many signal compression applications, the evolution of the signal over time can be represented by a sequence of random vectors with varying dimensionality. Frequently, the generation of such variable-dimension vectors can be modeled as a random sampling of another signal vector with a large but fixed dimension. Efficient quantization of these variable-dimension vectors is a challenging task and a critical issue in speech coding algorithms based on harmonic spectral modeling. We introduce a simple and effective formulation of the problem and present a novel technique, called variable-dimension vector quantization (VDVQ), where the input variable-dimension vector is directly quantized with a single universal codebook. The application of VDVQ to low bit-rate speech coding demonstrates significant gain in subjective quality as well as in rate-distortion performance over prior indirect methods.

Nonsquare transform vector quantization

Several techniques for low-rate speech coding have emerged, requiring quantization of the spectral magnitudes. The set of spectral magnitudes may be considered as a variable-dimension vector with dimension dependent on the pitch period. The authors present a technique called nonsquare transform vector quantization (NSTVQ). This technique addresses the problem of variable-dimension vector quantization by combining a fixed-dimension vector quantizer with a variable-sized nonsquare transform. The experimental results presented show that NSTVQ outperforms existing harmonic magnitude quantization techniques.