Adaptive Vector Quantization Algorithm Research Articles

High-Performance Iris Recognition for Mobile Platforms

In spite of a fact that many standalone iris recognition solutions are successfully implemented and deployed around the world, development of a reliable iris recognition solution capable to provide high recognition performance (both in biometric quality and speed) on mobile device is still an actual task. Main issues related to iris recognition in the mobile devices consist in uncontrollable capturing conditions and limitations in computation power. The aim of the proposed approach is to eliminate aforementioned issues by providing user with comprehensive feedback and, at the same time, performing the most computationally complex operations only on the images of the best quality. Key features of the proposed approach are multi-stage algorithm structure, novel iris image quality estimation and adaptive iris feature vector quantization algorithms. These features allow to achieve high recognition accuracy and real-time performance which are proved by experimental results.

Adaptive Vector Quantization for Lossy Compression of Image Sequences

In this work, we present a scheme for the lossy compression of image sequences, based on the Adaptive Vector Quantization (AVQ) algorithm. The AVQ algorithm is a lossy compression algorithm for grayscale images, which processes the input data in a single-pass, by using the properties of the vector quantization to approximate data. First, we review the key aspects of the AVQ algorithm and, subsequently, we outline the basic concepts and the design choices behind the proposed scheme. Finally, we report the experimental results, which highlight an improvement in compression performances when our scheme is compared with the AVQ algorithm.

A universal adaptive vector quantization algorithm for space-borne SAR raw data

Codebook of conventional VQ cannot be generally used and needs real time onboard updating, which is hard to implement in spaceborne SAR system. In order to solve this problem, this paper analyses the characteristic of space-borne SAR raw data firstly, and then utilizes the distortion function of multidimensional space as criterion, and finally the adaptive code book design algorithm is proposed according to the joint probability density function of the input data. Besides, the feasibility of the new algorithm in cascade with entropy coding and the robustness of the algorithm when error occurs during transmission are analysed based on the encoding and decoding scheme. Experimental results of real data show that codebook deriving from the new algorithm can be generally used and designed off-line, which makes VQ a practical algorithm for space-borne SAR raw data compression.

An Efficient Coding Method for Teleconferencing Video and Confocal Microscopic Image Sequences

In this paper we propose a three-dimensional vector quantization based video coding scheme. The algorithm uses a 3D vector quantization pyramidal code book based model with adaptive code book pyramidal codebook for compression. The pyramidal code book based model helps in getting high compression in case of modest motion. The adaptive vector quantization algorithm is used to train the code book for optimal performance with time. Some of the distinguished features of our algorithm are its excellent performance due to its adaptive behavior to the video composition and excellent compression due to codebook approach. We also propose an efficient codebook based post processing technique which enables the vector quantizer to possess higher correlation preservation property. Based on the special pattern of the codebook imposed by post-processing technique, a window based fast search (WBFS) algorithm is proposed. The WBFS algorithm not only accelerates the vector quantization processing, but also results in better rate-distortion performance. The proposed approach can be used for both teleconferencing videos and to compress images obtained from confocal laser scanning microscopy (CLSM). The results show that the proposed method gave higher subjective and objective image quality of reconstructed images at a better compression ratio and presented more acceptable results when applying image processing filters such as edge detection on reconstructed images. The experimental results demonstrate that the proposed method outperforms the teleconferencing compression standards H.261 and LBG based vector quantization technique.

Improved adaptive vector quantization algorithm using hybrid codebook data structure

Recently, Shen et al. [IEEE Transactions on Image Processing 2003;12:283–95] presented an efficient adaptive vector quantization (AVQ) algorithm and their proposed AVQ algorithm has a better peak signal-to-noise ratio (PSNR) than that of the previous benchmark AVQ algorithm. This paper presents an improved AVQ algorithm based on the proposed hybrid codebook data structure which consists of three codebooks—the locality codebook, the static codebook, and the history codebook. Due to easy maintenance advantage, the proposed AVQ algorithm leads to a considerable computation-saving effect while preserving the similar PSNR performance as in the previous AVQ algorithm by Shen et al. [IEEE Transactions on Image Processing 2003;12:283–95]. Experimental results show that the proposed AVQ algorithm over the previous AVQ algorithm has about 75% encoding time improvement ratio while both algorithms have the similar PSNR performance.

Method and system for reduction of quantization-induced block-discontinuities and general purpose audio codec

A method and system for reduction of quantization-induced block-discontinuities arising from lossy compression and decompression of continuous signals, especially audio signals. One embodiment encompasses a general purpose, ultra-low latency, efficient audio codec algorithm. More particularly, the invention includes a method and apparatus for compression and decompression of audio signals using a novel boundary analysis and synthesis framework to substantially reduce quantization-induced frame or block discontinuity; a novel adaptive cosine packet transform (ACPT) as the transform of choice to effectively capture the input audio characteristics; a signal-residue classifier to separate the strong signal clusters from the noise and weak signal components (collectively called residue); an adaptive sparse vector quantization (ASVQ) algorithm for signal components; a stochastic noise model for the residue; and an associated rate control algorithm. The invention further includes corresponding computer program implementations of these and other algorithms.

Soft-clipping postprocessor scaling decoded audio signal frame saturation regions to approximate original waveform shape and maintain continuity

An audio coder/decoder ("codec") that is suitable for real-time applications due to reduced computational complexity, and a novel adaptive sparse vector quantization (ASVQ) scheme and algorithms for general purpose data quantization. The codec provides low bit-rate compression for music and speech, while being applicable to higher bit-rate audio compression. The codec includes an in-path implementation of psychoacoustic spectral masking, and frequency domain quantization using the novel ASVQ scheme and algorithms specific to audio compression. More particularly, the inventive audio codec employs frequency domain quantization with critically sampled subband filter banks to maintain time domain continuity across frame boundaries. The input audio signal is transformed into the frequency domain in which in-path spectral masking can be directly applied. This in-path spectral masking usually results in sparse vectors. The ASVQ scheme is a vector quantization algorithm that is particularly effective for quantizing sparse signal vectors. In the preferred embodiment, ASVQ adaptively classifies signal vectors into six different types of sparse vector quantization, and performs quantization accordingly. The ASVQ technique applies to general purpose data quantization as well as to quantization in the context of audio compression. The invention also includes a "soft clipping" algorithm in the decoder as a post-processing stage. The soft clipping algorithm preserves the waveform shapes of the reconstructed time domain audio signal in a frame- or block-oriented stateless manner while maintaining continuity across frame or block boundaries. The invention includes related methods, apparatus, and computer programs.

Audio codec using adaptive sparse vector quantization with subband vector classification

An audio coder/decoder ("codec") that is suitable for real-time applications due to reduced computational complexity, and a novel adaptive sparse vector quantization (ASVQ) scheme and algorithms for general purpose data quantization. The codec provides low bit-rate compression for music and speech, while being applicable to higher bit-rate audio compression. The codec includes an in-path implementation of psychoacoustic spectral masking, and frequency domain quantization using the novel ASVQ scheme and algorithms specific to audio compression. More particularly, the inventive audio codec employs frequency domain quantization with critically sampled subband filter banks to maintain time domain continuity across frame boundaries. The input audio signal is transformed into the frequency domain in which in-path spectral masking can be directly applied. This in-path spectral masking usually results in sparse vectors. The ASVQ scheme is a vector quantization algorithm that is particularly effective for quantizing sparse signal vectors. In the preferred embodiment, ASVQ adaptively classifies signal vectors into six different types of sparse vector quantization, and performs quantization accordingly. The ASVQ technique applies to general purpose data quantization as well as to quantization in the context of audio compression. The invention also includes a "soft clipping" algorithm in the decoder as a post-processing stage. The soft clipping algorithm preserves the waveform shapes of the reconstructed time domain audio signal in a frame- or block-oriented stateless manner while maintaining continuity across frame or block boundaries. The invention includes related methods, apparatus, and computer programs.

Generalized threshold replenishment: an adaptive vector quantization algorithm for the coding of nonstationary sources

In this paper, we describe a new adaptive-vector-quantization (AVQ) algorithm designed for the coding of non-stationary sources. This new algorithm, generalized threshold replenishment (GTR), differs from prior AVQ algorithms in that it features an explicit, online consideration of both rate and distortion. Because of its online nature, GTR is more amenable to real-time hardware and software implementation than are many prior AVQ algorithms that rely on traditional batch training methods. Additionally, as rate-distortion cost criteria are used in both the determination of nearest-neighbor codewords and the decision to update the codebook, GTR achieves rate-distortion performance superior to that of other AVQ algorithms, particularly for low-rate coding. Results are presented that illustrate low-rate performance surpassing that of other AVQ algorithms for the coding of both an image sequence and an artificial non-stationary random process. For the image sequence, it is shown that (1) most AVQ algorithms achieve distortion much lower than that of nonadaptive VQ for the same rate (about 1.5 b/pixel), and (2) GTR achieves performance substantially superior to that of the other AVQ algorithms for low-rate coding, being the only algorithm to achieve a rate below 1.0 b/pixel.

An on-line universal lossy data compression algorithm via continuous codebook refinement .III. Redundancy analysis

For pt.II see ibid., vol.42, p.822-36 (1996). The Gold-washing data compression algorithm is an adaptive vector quantization algorithm with vector dimension n. In this paper, a redundancy problem of the Gold-washing data compression algorithm is considered. It is demonstrated that for any memoryless source with finite alphabet A and generic distribution p and for any R>0, the redundancy of the Gold-washing data compression algorithm with dimension n (defined as the difference between the average performance of the algorithm and the distortion-rate function D(p,R) of p) is upper-bounded by |/sub /spl delta/R///sup /spl delta//D(p,R)|((|A|+2/spl xi/+4 log n)/2n)+/spl sigma/(logn/n) where /sub /spl delta/R///sup /spl delta//D(p,R) is the partial derivative of D(p,R) with respect to R, |A| is the cardinality of A, and /spl xi/>0 is a parameter used to control the threshold in the Gold-washing algorithm. In connection with the results of Zhang, Yang, and Wei (see ibid., vol.43, no.1, p.71-91, 1997) on the redundancy of lossy source coding, this shows that the Gold-washing algorithm has the optimal convergence rate among all adaptive finite-state vector quantizers.

Adaptive vector quantization for picture coding using neural networks

The paper presents applications of neural network algorithms to the design of an adaptive vector quantizer. Vector quantization has been applied to the problem of displaying natural images with a reduced set of colors (colormap) and to the interframe coding of image sequences. The first step was to test classical Linde Buzo Gray (LGB), self organizing feature maps (SOFM) and Competitive Learning (CL) algorithms for the codebook design. The best results for the reconstructed quality image and the computational time are obtained using a CL algorithm with a new initialization strategy that solves the problem of underutilized nodes. An adaptive vector quantization algorithm is proposed and tested in a motion compensated image coder. The results of the simulations are very promising. In fact the coder performance, compared with that using a fixed VQ, is considerably improved and the subjective quality of the coded images is much better than that obtained using standard vector quantization, especially when rapid motion is present in the scene. >

Comparison between several adaptive search vector quantization schemes and JPEG standard for image compression

This paper introduces an adaptive search vector quantization (VQ) algorithm, hierarchical adaptive search VQ (HASVQ) and compares its performance against several previously proposed adaptive search image compression algorithms. These algorithms are: the JPEG standard, adaptive tree search vector quantization (ATSVQ), multi-stage adaptive search vector quantization (MASVQ). The bit-rate examined is from 0.625 to 1.25 bits per pixel (bpp). The results indicate that HASVQ outperforms all the other algorithms over the entire range of bit-rates examined. For higher bit-rates MASVQ performs next to the HASVQ algorithm and is, in-turn, followed by the JPEG standard. ATSVQ performs well at lower bit-rates, however its performance at higher bit-rates is suppressed by not having an adequate codebook size. >

Computational RAM implementation of an adaptive vector quantization algorithm for video compression

Vector quantization (VQ) is a promising technique for low-bit rate image and video compression. Adaptive VQ-based video compression algorithms have been reported in the literature. This paper proposes an adaptive codebook replenishment VQ algorithm using index-based motion estimation (AVQ+ME) for low-bit rate video compression. The proposed technique has been implemented on a computational* RAM (C*RAM) SIMD structure.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Improved techniques for single-pass adaptive vector quantization

Constantinescu and Storer presented a new single-pass adaptive vector quantization algorithm that learns a codebook of variable size and shape entries; they presented experiments on a set of test images showing that with no training or prior knowledge of the data, for a given fidelity, the compression achieved typically equals or exceeds that of the JPEG standard. This paper presents improvements in speed (by employing K-D trees), simplicity of codebook entries, and visual quality with no loss in either the amount of compression or the SNR as compared to the original full-search version.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Quantitative properties of Kohonen's self‐organizing maps as adaptive vector quantizers

AbstractKohonen's model as the self‐organizing model for the neural network can be considered as a kind of adaptive vector quantization algorithm. Numerous reports have been presented on the application of the model to practical problems. Although some results have been presented for the theoretical properties of Kohonen's model, many properties remain to be clarified.Among various properties of Kohonen's model as an adaptive vector quantization algorithm, this paper considers the problem of how the reference vectors are placed according to the probability distribution of the input signal. Considering the limit where the number of reference vectors is increased to infinity, this problem can be discussed theoretically as the distribution of the reference vectors.Due to the effect of the “learning by neighborhood,” which is the feature of Kohonen's model, the property of the Kohonen model differs quantitatively from the property of the ordinary vector quantization algorithm. This paper discusses quantitatively the properties of Kohonen's model using the average learning equation.