Differential Pulse Code Modulation System Research Articles

A Comparative Analysis of Image Compression using PCM and DPCM

The quality of the signal is essential in digital communication and signal processing. The transmission channel is also important. Modulation is used for effectively transmission of signal. There exist several types of modulation techniques. One of such is the pulse code modulation (PCM). The performance of PCM is however affected by quantization error and noise in the transmission channel, which affects the quality of the output. Against this backdrop, this paper presents the use of differential pulse code modulation (DPCM) so as to address the limitation of pulse code modulation. The simulation environment is MATLAB 2018a. The MATLAB Simulink is used to design the PCM and DPCM systems using appropriate digital processing blocks. The DPCM system shows a significant improvement in terms of error reduction and quality of output.

Quality Verification of Audio and Image Modulation by the Simulation of PCM, DM and DPCM Systems

Modulation is a process through which a message has to pass in order to be effectively transmitted. However, there are some limitations to Pulse Code Modulation and Delta Modulation that can cause data redundancy, quantization error, slope overload distortion and granular noise which result in a bad communication process. Throughout the past few years, Pulse Code Modulation (PCM), Delta Modulation (DM) and Differential Pulse Code Modulation (DPCM), in digital communication systems, have proven to have unparalleled advantages over analog communication systems; this is in terms of error minimization and distances of transmission enhancements. Delta Modulation, a simplified version of Pulse Coded Modulation also pauses major problems in noise and quantization error. Consequently, and to combat the arising problems, communication engineers have developed newly adaptive compression and modulations techniques for better digital transmission. One of these innovative systems is the Differential Pulse Coded Modulation (DPCM) that can solve the aforementioned problems. Thus the focal point of this article is to explore the simulation of these systems using Simulink (The Math Works, Inc., USA). Eventually, the systems are tested on both image and audio inputs to prove the superiority of DPCM over DM and PCM systems in reducing noise and increasing the signal to quantization noise ratio, thus insuring a smooth and successful transfer of data.

DESIGN AND ANALYSIS OF DIFFERENT TECHNIQUES FOR ANALOG-TO-DIGITAL CONVERSION OF VIBRATION SIGNALS FOR WIRELESS MEASUREMENT SYSTEMS

The aim of this paper is to design, analyze and compare four different systems for ADC (analog-to-digital conversion) of vibration signals. Measurement of vibration signals is of particular importance in many areas, such as predictive maintenance or structural health monitoring. Wireless systems for vibration measurements becomes very topical, due to much easier and cheaper installation compared to wired systems. Due to the lack of transmission bandwidth and energy in wireless measurement systems, the amount of digital data being sent has to be reduced; hence, we have to apply ADC systems that can achieve the required digital signal quality, reducing the bit-rate. Four ADC systems are analyzed, for possible application in wireless measurement systems: PCM (pulse code modulation) based on uniform quantization; DPCM (differential PCM) to exploit high correlation of vibration signals; two adaptive ADC systems to cope with significant variations of characteristics of vibration signals in time - APCM (adaptive PCM) with adaptation on variance and ADPCM (adaptive DPCM), with double adaptation (both on variance and correlation). These ADC models are designed and optimized specifically for vibration signals, based on the analysis of 20 vibration signals from a referent database. An experiment is done, applying designed ADC systems for digitalization of vibration signals. APCM, DPCM and ADPCM systems allow significant bit-rate reduction compared to the PCM system, but with the increasing of complexity, hence the compromise between the bit-rate reduction and complexity is needed.

The Probability of Stability Estimation of an Arbitrary Order DPCM Prediction Filter: Comparison between the Classical Approach and the Monte Carlo Method

This paper presents the stability analysis of the linear recursive (prediction) filters with higher order predictors in a DPCM (differential pulse-code modulation) system, where traditional methods become too difficult and complex. Stability conditions for the third- and fourth-order predictor are given by using the Schur–Cohn stability criterion. The probability of stability estimation is performed by using the Monte Carlo method. Verification of the proposed method is performed for lower order predictors (the first- and second-order). We calculated numerical values of the probability of stability for higher order predictors and previously experimentally obtained parameters. With large enough number of trials (samples) in Monte Carlo simulation, we reach the desired accuracy. DOI: http://dx.doi.org/10.5755/j01.itc.46.2.14038

Robustness of the Prediction Filter in Differential Pulse Code Modulation System

Robustness of the differential pulse code modulation system with the first and second order predictor is considered in this paper. Special focus is on a robust stability of prediction filter with regard to predictor coefficients. Generalization of robustness in classical sense is performed and suitable relations for calculating probability of robustness are derived. The proposed method for robustness estimation is used for the first and second order prediction filters in the case of speech signal. DOI: http://dx.doi.org/10.5755/j01.eie.22.5.16347

Dynamical Behavior in a DPCM System with an Order 2 Predictor. Case of a Constant Input

This paper deals with a differential pulse code modulation (DPCM) transmission system. It includes an encoder which uses a coupling between a transversal predictor and a nonlinear quantizer. This paper studies, from a dynamical point of view, an order 2 DPCM system modeled via a noninvertible two-dimensional map. Chaotic phenomena can be observed; depending on the kind of application, it can be interesting to avoid chaos or to use it. In both cases, it is necessary to understand the mechanisms which give rise to it. For that, the usual tools of chaotic dynamics are used (bifurcation structures, critical and invariant manifolds).

DPCM-based vibration sensor data compression and its effect on structural system identification

Due to the large scale and complexity of civil infrastructures, structural health monitoring typically requires a substantial number of sensors, which consequently generate huge volumes of sensor data. Innovative sensor data compression techniques are highly desired to facilitate efficient data storage and remote retrieval of sensor data. This paper presents a vibration sensor data compression algorithm based on the Differential Pulse Code Modulation (DPCM) method and the consideration of effects of signal distortion due to lossy data compression on structural system identification. The DPCM system concerned consists of two primary components: linear predictor and quantizer. For the DPCM system considered in this study, the Least Square method is used to derive the linear predictor coefficients and Jayant quantizer is used for scalar quantization. A 5-DOF model structure is used as the prototype structure in numerical study. Numerical simulation was carried out to study the performance of the proposed DPCM-based data compression algorithm as well as its effect on the accuracy of structural identification including modal parameters and second order structural parameters such as stiffness and damping coefficients. It is found that the DPCM-based sensor data compression method is capable of reducing the raw sensor data size to a significant extent while having a minor effect on the modal parameters as well as second order structural parameters identified from reconstructed sensor data.

Differential pulse code modulation predictor design procedure using a genetic algorithm

Digitized images are used widely in current industrial applications; however, digital images cost a huge storage capacity, leading to transmission bandwidth constraints. Therefore, an image compression technique was developed to solve this drawback. Differential pulse code modulation (DPCM) is the most widely used method among predictive image coding schemes. It is based on the notation of quantizing a prediction error signal after the prediction operation to reduce the original data complexity. The design of the predictor influences the performance of the whole DPCM. Therefore, the predictor constitutes a central topic in the DPCM system. We make efforts toward the design of a predictor by using a genetic algorithm (GA). By the operations of crossover and mutation, several predictor coefficients are yielded after hundreds of generations. The predictor generated by the GA outperforms the current eight different predictive schemes in Joint Photographic Experts Group lossless (JPEG-LS) still image compression standard. Because the JPEG-LS compression scheme is currently the most widely used lossless compression method, our results contribute to the applications of compression.

Dynamical study of a second-order DPCM transmission system modeled by a piecewise-linear function

This paper analyzes the behavior of a second-order differential pulse code modulation (DPCM) transmission system when the nonlinear characteristic of the quantizer is taken into consideration. In this way, qualitatively new properties of the DPCM system have been unraveled, which cannot be observed and explained if the nonlinearity of the quantizer is neglected. For the purpose of this study, a piecewise-linear nondifferentiable quantizer characteristic is considered. The resulting model of the DPCM is of the form of iteration equations (i.e., map), where the inverse iterate is not unique (i.e., noninvertible map). Therefore, the mathematical theory of noninvertible maps is particularly suitable for this analysis, together with the more classic tools of nonlinear dynamics. This study allowed us, in addition, to show, from a theoretical point of view, some new properties of nondifferentiable maps, in comparison with differentiable ones. After a short review of noninvertible maps, the presented methods and tools for noninvertible maps are applied to the DPCM system. An original algorithm for calculation of bifurcation curves for the DPCM map is proposed. Via the studies in the parameter and phase plane, different nonlinear phenomena such as the overlapping of bifurcation curves causing multistability, chaotic behavior, or multiple basins with fractal boundary are pointed out. All observed phenomena show a very complex dynamical behavior even in the constant input signal case, discussed here.

Optimal use of Markov models for DPCM picture transmission over noisy channels

Joint source/channel decoders that use the residual redundancy in the source are investigated for differential pulse code modulation (DPCM) picture transmission over a binary symmetric channel. Markov sequence decoders employing the Viterbi algorithm that use first-order source statistics are reviewed, and generalized for decoders that use second-order source statistics. To make optimal use of the source correlation in both horizontal and vertical directions, it is necessary to generalize the conventional Viterbi decoding algorithm for a one higher-dimensional trellis. The paths through the trellis become two-dimensional "sheets", thus, the technique is coined "sheet decoding". By objective [reconstruction signal-to-noise ratio (SNR)] and subjective measure, it is found that the sheet decoders outperform the Markov sequence decoders that use a first-order Markov model, and outperform two other known decoders (modified maximum a posteriori probability and maximal SNR) that use a second-order Markov model. Moreover, it is found that the use of a simple rate-2/3 block code in conjunction with Markov model-aided decoding (MMAD) offers significant performance improvement for a 2-bit DPCM system. For the example Lenna image, it is observed that the rate-2/3 block code is superior to a rate-2/3 convolutional code for channel-error rates higher than 0.035. The block code is easily incorporated into any of the MMAD DPCM systems and results in a 2-bit MMAD DPCM system that significantly outperforms the noncoded 3-bit MMAD DPCM systems for channel-error rates higher than 0.04.

Nonsymmetric sources and optimum signal selection

There are some applications where the information source is nonsymmetric. Here, it is shown that differential pulse code modulation (DPCM) of video signals results in a nonsymmetric source. Previous works on 2-D signal design have been focused around equally likely sources. In this paper, an iterative algorithm for minimum error signal design subject to average power (or peak power) constraint is presented. Then, design of minimum cost signal sets is addressed, and application to a DPCM system for picture transmission is developed. Comparisons are made between the nonsymmetric and symmetric signal design in one case and between minimum error signal design and minimum cost signal selection in the other case, for the transmission of DPCM-coded signals. It is seen that nonsymmetric signal design gains significant improvements over the equally likely signal selection when the channel is noisy. Minimum cost signal design also demonstrated some improvement relative to minimum error signal selection over noisy channels.

Nonlinear prediction with neural networks applied to 1-D DPCM image coding

Neural net architectures, with a hidden layer or functional links, have been utilized to generate prediction for one-dimensional (1-D) differential pulse code modulation (DPCM), applied to still image coding. In this approach, the predictor is designed by supervised training based on a typical sequence of pixel values; i.e., the values of the coefficients of the predictor are determined by training on examples. Nonlinear as well as linear correlations are exploited. Computer simulation experiments have been carried out to evaluate the resulting performance. At a transmission rate of 1 bit/pixel, for the images Lena and Baboon, the 1-D neural network DPCM provides a 4.17- and 3.74-dB improvement in peak SNR, respectively, over the standard linear DPCM system.

Applications of a DPCM system with median predictors for image coding

A differential pulse code modulation (DPCM) system employing a median predictor, which is called the predictive-median-DPCM (PM-DPCM), is proposed. It is shown that the PM-DPCM is resistant to transmission errors. Specifically, it is observed that transmission errors are often isolated and not propagated in the PM-DPCM images. The characteristics of PM-DPCM are analyzed, and its performance is compared with those of other DPCM systems through experiments. 1-D and 2-D PM-DPCM systems are defined and their properties are examined. Some statistical properties of PM-DPCM are derived. >

Neural network approach to DPCM system design for image coding

This paper presents a neural network approach to differential pulse code modulation (DPCM) design for the encoding of images. Instead of traditional algorithms for the computation of the relevant coefficients, such as the autocovariance and autocorrelation methods, the predictor is designed by supervised training of a neural network on examples, i.e. on a typical sequence of pixel values. This allows the use of nonlinear as well as linear correlations. Efficient and fast neural net architectures, for nonlinear one-dimensional DPCM (NNDPCM) as well as two-dimensional adaptive DPCM (NNADPCM), have been designed and applied to still image coding. Computer simulation experiments have shown that the resulting encoders work very well. At a transmission rate of 1 bitpixel, the 1-D NNDPCM offers an advantage of about 4 dB in peak signal-to-noise ratio over the standard linear DPCM system. At a bit rate of 0.525 bit/pixel, the 2-D NNADPCM achieves 29.5 dB for the 512 × 512 Lena image, while there is little visible distortion in the reconstructed image. This performance is comparable to that of the best schemes known to date, whether DPCM based or not, while maintaining a lower encoding complexity. Furthermore, this establishes that there is substantial amount of nonlinear content available for 1-D and 2-D prediction in DPCM image coding.

Stability of the DPCM transmission system

Bounded input-bounded output stability of the differential pulse code modulation (DPCM) transmission system is investigated. The output is calculated by a nonlinear feedback loop. In the feedforward path the quantizer characteristic can be taken as continuous. It exhibits a threshold and has a linear part with a variable slope p greater than 1. In the feedback path the linear filter R is recursive. Stability of R is sufficient but not necessary to ensure stability of the DPCM system. Conditions for stability of an order 1 predictor are determined. Comparison with the classical concept of stability for linear systems displays three specific features of the DPCM case: 1) the conditions on the prediction coefficient are less stringent: 2) the kind of stability is weaker; and 3) the negative case has a better stability than the positive case. As a function of the quantizer slope, the maximum prediction coefficient exhibits a maximum. The corresponding value p/sub opt/ delimits two ranges. For smaller slopes the quantizer threshold effect degrades the stability. At the optimum, the range of stability is the same with and without the quantizer threshold.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Comments on "A subjective evaluation of noise-shaping quantization for adaptive intra-/interframe DPCM coding of color television signals" (by B. Girod et al.)

In the above named work by B. Girod et al. (see ibid., vol.36, no.3, p.332-46, 1988), a stability condition for a DPCM (differential pulse code modulation) system with additional quantization error noise shaping has been proposed. In the present work, a correction of this stability condition is derived. >

Nonlinear prediction in image coding with DPCM

In contrast to the traditional linear differential pulse code modulation (DPCM) design for the encoding of images, a new, nonlinear, neural network-based, DPCM technique has been devised. The predictor is designed by supervised training, based on a typical sequence of pixel values in an image. A function link neural network architecture has been used to design the predictor for one dimensional (1-D) DPCM. Computer simulation experiments in still image coding have shown that the resulting encoders work very well. At a transmission rate of 1 bit/pixel, for the image LENA, the 1-D neural network DPCM provides a 4.2 dB improvement in SNR over the standard linear DPCM system.

Backward adaptive prediction as spectral analysis within a closed loop

Backward adaptive prediction in differential pulse code modulation (DPCM) systems is represented as a problem of spectral analysis within a closed loop. Using this spectral analysis approach, the backward adaptive all-pole predictor in classic DPCM is shown to perform well, given good initial conditions and a slowly varying DPCM system input. However, when it loses track, the all-pole predictor is shown to have a tendency to track itself rather than changes in the input signal transfer function. The use of a pole-zero predictor improves the situation since the backward adaptation of the zeros is clearly dependent upon the input signal transfer function and thus should respond to changes or nonstationarities in the DPCM system input. The backward adaptation of the poles in the pole-zero predictor is able to track changes in the system input through variations in the predictor zeros. The results provide additional motivation for including zeros in backward adaptive predictors for DPCM.

Self-Orthogonal Convolutional Coding for the DPCM-AQB Speech Encoder

A fixed-tap differential pulse code modulation (DPCM) system with a robust backward-adaptive Jayant quantizer is investigated for speech encoding at 16-40 kbits/s using binary phase shift keying over an additive white Gaussian noise channel. The performance of this system becomes unacceptable as the channel bit error rate <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(P_{b})</tex> approaches 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> . Using high-rate, long constraint length, self-orthogonal convolutional codes, the DPCM system performance is much-improved for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10^{-4} &lt; P_{b} &lt; 10^{-2}</tex> depending on the transmitted data rate. The use of high-rate <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(n - 1)/n, n = 2,3,4,</tex> , and 5 codes minimizes the number of bits allocated to channel coding, and decoding complexity is reduced by employing self-orthogonal codes which admit threshold decoding. Subjectively, while there is additional quantization noise with channel coding, the irritating popping and squeaking sounds due to channel errors are eliminated.

Sequential noise spectral shaping in ADPCM

The output speech from a fixed-tap differential pulse code modulation system with adaptive quantization and adaptive noise spectral shaping (NSS) is compared for block-adaptive and sequentially adaptive NSS filters. Block-adaptive systems incorporate a delay which can build up in analog communications systems and cause echoing problems. The buildup of delay can be eliminated by implementing the sequentially adaptive Kalman algorithm in the NSS filter. Simulations are performed for 4-, 8-, and 16-level quantizers with fourth- and ninth-order Kalman adaptation of the NSS filter. A block-adaptive system is implemented as a reference. Subjective listening tests and spectrograms show the Kalman algorithm to be a viable alternative to the block-adaptive algorithms. Signal-to-noise ratios are also given.