Associative computation circuits for real-time processing of satellite communications and image pattern classification

We propose a low bit-rate coding algorithm for wireless communication based on multi-stage hierarchical vector quantization, motion compensation and differential pulse code modulation. Our method adapts bit allocation to spatial and temporal correlation. Conventional schemes based on the discrete cosine transform (DCT) need a large amount of computation on both encoding and decoding. On the other hand, our proposed method consists of addition, subtraction and shift operation. It does not use multiplication. It can decode in real time on a conventional serial processor. Encoding by vector quantization (VQ), however, consumes a large amount of computation. We developed a new LSI to accelerate VQ. Our scheme can send 10 frames of QCIF video sequences through a 29.2 kbps line. The quality of reconstructed image is over 30 dB.

In this paper, we first propose a new embedded multilevel block truncation coding (BTC) technique. Unlike Differential pulse code modulation (DPCM), Vector quantization (VQ), and general multilevel BTC algorithms which determine the image quality at a time, the embedded multilevel BTC improves the image quality largely and progressively until obtaining an image with excellent quality. In order to reduce the bit rate efficiently, we propose a perception model and utilize it to develop a pruning algorithm. The pruning algorithm removes the useless information, which the human eyes are not sensitive, generated by the embedded multilevel BTC. The simulation results indicate that the bit rate with the proposed method is much less than that with the DPCM and general multilevel BTC under the same objective criterion, PSNR, or subjective criterion. This paper also shows that the computation complexity of the proposed method is much less than that with VQ under the same high quality reconstructed image.

Embedded wavelet encoders possess a number of interesting features for video encoding applications. Among those, we can mention-the ability to encode a picture with precise control over the bit-rate. This differs significantly from the DCT-based methods adopted in the MPEG-4 standard, in which the rate cannot be directly set, being controlled by the quantizer step size instead. We investigate, rate-control strategies for embedded wavelet encoders in an MPEG-4 framework, that take advantage of the precise control that can be obtained for the bit-rate in such coders. We also investigate the use of successive approximation vector quantization in them for replacing the traditionally used successive approximation scalar quantization. The results are encouraging showing that both the use of vector quantization and the adoption of an adequate rate control strategy can improve both objective and subjective quality of video sequences encoded using embedded wavelet encoders.

A new form of predictive quantization, dubbed quantized predictive coding (QPC), is presented. One version uses variable-length coding and another uses fixed-length coding. For autoregressive sources, it is shown both analytically and experimentally that the performance of QPC with (without) entropy-coding is, essentially, the same as that of DPCM (differential pulse code modulation) with uniform quantization with (without) entropy-coding. The principal advantage of QPC is that, unlike DPCM, it is a priori designed for digital implementation. As a result, its digital implementation is simpler than DPCM and suffers no loss in performance due to round-off errors. >

A new hybrid coding technique is introduced which is based on the Discrete Cosine Transform, interframe DPCM, conditional replenishment, detection of significant subareas in the transform domain, adaptive quantization, adaptive Huffman coding and postbuffer control. This coder concept is the result of a comparison of several coding methods including uniform/nonuniform quantization, constant/variable word length coding and prebuffer/post-buffer control schemes. An important feature of the presented coder is the selection of transform coefficients within each block which are grouped in subareas for adaptive entropy coding. The components of the coder are described and its excellent performance is demonstrated by means of SNR-measurements and a typical videoconference sequence.

Vector quantization (VQ) based lossy compression techniques are ideally suited for image archival and distribution applications due to their asymmetrical computational properties. The practical use of VQ is however limited due to complications arising from design and maintenance of codebooks. Model-based VQ (MVQ) is a variant of VQ with the computational properties of VQ but without complications from explicit handling of codebooks. In MVQ, error models combined with a human visual system (HVS) model are used to generate an implicit model codebook. The decoder generates same codebook using the model parameters thus eliminating the need for training algorithm, transmitting and maintaining the codebooks. This algorithm was tested on several NASA images and results were published, M. Manchar et al. (1995). The present authors describe progressive MVQ along the lines of progressive VQ and evaluate its performance on NASA images.

The use of vector quantization (VQ) in isolated-word recognition of a 20-word vocabulary is examined. A separate sequence of VQ code books is designed for each word in the recognition vocabulary and input words are classified by performing VQ and finding the sequence of code books that achieve the smallest average distortion. In this paper, critical parameters are noted and the results of parameter studies are presented.

An ideal image compression technique for image data archival, retrieval and distribution would be one with the asymmetrical computational requirements of vector quantization (VQ), but without the complications arising from VQ codebooks. Codebook generation and maintenance are stumbling blocks which have limited the use of VQ as a practical image compression algorithm. Model-based VQ (MVQ), a variant of VQ described here, has the computational properties of VQ but does not require explicit codebooks. The codebooks are internally generated using mean removed error and human visual system (HVS) models. The error model assumed is the Laplacian distribution with mean, (lambda) , computed from a sample of the input image. A Laplacian distribution with mean, (lambda) , is generated with a uniform random number generator. These random numbers are grouped into vectors. These vectors are further conditioned to make them perceptually meaningful by filtering the DCT coefficients from each vector. The DCT coefficients are filtered by multiplying by a weight matrix that is found to be optimal for human perception. The inverse DCT is performed to produced the conditioned vectors for the codebook. The only image dependent parameter used in the generation of codebook is the mean, (lambda) , that is included in the coded file to repeat the codebook generation process for decoding.

Our earlier algorithm (1996) on neural clustering networks (proved to provide globally optimal cluster solutions) is improved herein for considerably faster convergence. The neural network approach and the sort of clustering (vector quantization) are explained. Then the new method is introduced. Computational results are given which demonstrate that the globally optimal solution may be reliable, obtained by the fast algorithm whose convergence rate is of the same order as that of the K-means clustering algorithm. In specific cases the new algorithm may be even faster than K-means. Latent risks of a poor performance of K-means are visualized and consequences for the potential use of vector quantization are discussed.

Video compression has great significance in the communication of motion pictures. Video compression techniques try to remove the different types of redundancy within or between video sequences. In the temporal domain, the video compression techniques remove the redundancies between the highly correlated consequence frames of the video. In the spatial domain, the video compression techniques remove the redundancies between the highly correlated consequence pixels (samples) in the same frame. Evolving neural-networks based video coding research efforts are focused on improving existing video codecs by performing better predictions that are incorporated within the same codec framework or holistic methods of end-to-end video compression schemes. Current neural network-based video compression adapts static codebook to achieve compression that leads to learning inability from new samples. This paper proposes a modified video compression model that adapts the genetic algorithm to build an optimal codebook for adaptive vector quantization that is used as an activation function inside the neural network’s hidden layer. Background subtraction algorithm is employed to extract motion objects within frames to generate the context-based initial codebook. Furthermore, Differential Pulse Code Modulation (DPCM) is utilized for lossless compression of significant wavelet coefficients; whereas low energy coefficients are lossy compressed using Learning Vector Quantization (LVQ) neural networks. Finally, Run Length Encoding (RLE) is engaged to encode the quantized coefficients to achieve a higher compression ratio. Experiments have proven the system’s ability to achieve higher compression ratio with acceptable efficiency measured by PSNR.

Vector quantization (VQ) has long been recognized for its potential in image compression due to its ability to represent high-dimensional signal vectors compactly. However, the adoption of VQ in modern codecs has been hindered by practical limitations such as fixed-rate codebooks, lack of scalability, and inefficiency in entropy coding. In this work, we revisit VQ from a practical perspective and explore effective techniques to integrate it into modern image coding frameworks. We propose a rate-distortion-oriented (RD-oriented) design of tree-structured vector quantization (TSVQ) that enables flexible codebook construction at multiple bit rates through a growing method. Moreover, we introduce a cascade VQ strategy to promote the VQ ability by successively quantizing residual vectors, and propose a switching point decision method based on RD slope analysis. Our proposed methods enable scalable and RD-optimized vector quantization, demonstrating the potential of being applied to practical image coding frameworks.

This paper presents a hybrid image compression scheme based on a block based compression algorithm referred to as Vector Quantization (VQ) combined with the Differential Pulse Code Modulation (DPCM) technique. The proposed image compression technique called the PVQ scheme results in enhanced image quality as compared to the standalone VQ. The generated codebooks for the PVQ scheme are more robust for image coding than that of the VQ. This made our system a suitable candidate for developing on chip image sensor with integrated data compression processor. The proposed system was validated through FPGA implementation. The resulting implementation achieved good compression and image quality with moderate system complexity.

Adaptive Vector Quantization for Speech Spectrum Coding

The technique of vector quantization has been widely applied in the area of speech coding and has recently been introduced into the area of speech recognition. For the conventional statistical pattern recognition word recognizer using LPC feature sets as the analysis frames, the use of vector quantization leads to a large reduction in computation for the dynamic time warping pattern matching, and a concomittant small increase in average word error rate. A second technique that has been recommended for improving the performance of isolated word recognizers is the addition of temporal energy information into the distance metric for comparing frames of speech. It has been shown that the information in the prosodic energy contour complements the segmental information of the LPC spectrum, thereby providing small but consistent improvements in performance for small word vocabularies. In this talk we present results of a series of speaker independent, isolated word recognition tests using a 10-word digits vocabulary and a 129-word airlines vocabulary. We show the effects, on recognition accuracy, of adding both vector quantization and temporal energy in various combinations, to the recognition paradigm.

A conceptually new approach for describing the excitation signal in multipulse-excited LPC has been developed. The information needed to determine the excitation for each frame is transmitted to the receiver in the form of a crosscorrelation function. With the help of the LPC filter parameters, the receiver computes the excitation pulse sequence needed to generate the reconstructed speech waveform. Both the crosscorrelation function and the set of LPC parameters are coded by vector quantization. We utilize a classified codebook assignment technique in VQ for the crosscorrelation. This paper describes the coder structure, the use of vector quantization, and the results of computer simulation studies.