Differential Pulse Code Modulation Technique Research Articles

A new compressive sensing video coding framework based on Gaussian mixture model

In this paper, we specifically design an efficient compressive sensing video (CSV) coding framework for the CSV system, by considering the distribution characteristics of the CSV frame. To explore the spatial redundancy of the CSV, the CSV frame is first divided into blocks and each block is modeled by a Gaussian mixture model (GMM), and then it is compressed by a product vector quantization. We further explore the temporal redundancy of the CSV by encoding the adjacent CSV frames by the differential pulse code modulation technique and the arithmetic encoding technique. Experiment results show that the proposed CSV coding solution maintains low coding complexity, which is required by the CSV system. Meanwhile, it achieves significant BD-PSNR improvement by about 7.13–11.41dB (or equivalently 51.23–66.96% bitrate savings) compared with four existing video coding solutions, which also have low computational complexity and suit for the CSV system.

Efficient lossless multi-channel EEG compression based on channel clustering

With the growth of telemedicine systems, transferring a large number of medical signals such as for an EEG is a critical challenge. Intelligent analyzing systems, responsible for analyzing medical signals, are a very important part of any telemedicine system. These systems need data with high quality in order to detect abnormal events and diseases. Lossless compression methods play an important role when coding medical signals for telemedicine systems since the data remains unchanged. Multi-channel EEG signals for medical applications are usually acquired by a number of electrodes placed on different parts of the scalp. According to electrode placements, it is necessary to take into account their multi-channel structure to propose efficient compression methods. This paper uses inter-channel and intra-channel correlations to propose an efficient and simple lossless compression method. In the first stage, a differential pulse code modulation technique is used as a preprocessing step for extracting intra-channel correlation. Subsequently, channels are grouped in different clusters, and the centroid of each cluster is calculated and coded by arithmetic coding. In the second stage, the difference between the centroid and the data of channels in each cluster is calculated and compressed by arithmetic coding. The proposed method is capable of lossless EEG signal compression with a higher compression rate than existing methods.

Color Image Compression Using DPCM with DCT, DWT and Quadtree Coding Scheme

This paper is concerned with the design and implementation of a compression method for color image. This method based on Differential Pulse Code Modulation (DPCM), Discrete Cosine Transform (DCT) , Discrete Wavelet Transform (DWT) and Quadtree Coding Scheme. As a first step the DPCM technique is used to isolate blocks of an image into correlated and uncorrelated blocks. The isolated correlated blocks have been compressed using DCT based compression method then each block has been embedded with zeros on the original image. Each uncorrelated block has been compressed using DWT based method and put the compressed block in its location on the original image. Then, the result (i.e., the zeros blocks and compressed blocks with DWT) coded using Quadtree spatial coding. The output from DWT based and DCT based passed through shift coding stage to gain a possible further compression. The performance results of proposed hybrid algorithms produces better quality of image in terms of Peak-Signal-to-Noise Ratio (PSNR) with a higher Compression Ratio (CR) compared to standalone DCT or DWT. The PSNR values of the reconstructed images after applying proposed system are ranged from 30.62 to 40.95dB and CR on average, have been reduced to be around 1:19.6 of the size of the original image.

Nonlinear backward ADPCM speech coding using kernel methods and online sparsification

ABSTRACTModeling using neural networks and Volterra expansion are widely used in signal processing to account for nonlinear characteristics inherent in natural signals. In spite of their good performance in a wide range of applications, adaptive implementations of these models suffer from severe problems of a large number of parameters, slow convergence, and the risk of being trapped in local minima. Furthermore, inherent instability of the Volterra filters makes them unsuitable for applications that require synthesis or reconstruction, for example, speech coding. Nonetheless, kernel methods in the framework of reproducing kernel Hilbert spaces are emerging solutions that can tackle these problems by extending linear algorithms to spaces created by nonlinear mapping of the input signal. In this paper, we address the problems encountered in nonlinear adaptive coding by employing, for the first time, several kernel adaptive algorithms in the framework of speech coding using the adaptive differential pulse code modulation technique. Particular attention is focused on the effects of various sparsification techniques on the performance of the resultant algorithms. Simulation results show that utilizing kernel methods results in an average improvement of up to 3.4 dB in the signal‐to‐noise ratio of the reconstructed speech. This is while a lower improvement of up to 1.4 dB is achieved by exploiting Volterra filters at the cost of a demanding increase in the complexity, a slight increase in the bit‐rate and introducing a relatively long delay. Copyright © 2012 John Wiley & Sons, Ltd.

Investigations on colour coding in an object-oriented analysis-synthesis coder

In this paper, a new colour coding algorithm called Hybrid Adaptive DCT/DPCM Colour Coding is presented which encodes the colour parameters of objects in an object-oriented analysis-synthesis coder with a hybrid scheme, where either a DPCM (Differential Pulse Code Modulation) technique or a DCT (Discrete Cosine Transform) is used whichever allows a more efficient coding. In experimental results, the coding efficiency of the Hybrid Adaptive DCT/DPCM Colour Coding is compared to purely block-oriented DCT coding and region-oriented transform coding for typical videophone sequences at data rates of about 64 kbit/s. The relative gain concerning the average bit-rate at a fixed image quality is about 5% compared to region-oriented transform coding and 41% compared to block-oriented DCT coding. Beside its coding efficiency, Hybrid Adaptive DCT/DPCM Coding can easily be realized by fast algorithms of low computational complexity.

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.