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Music rhythm detection and tracking is an important part of the music comprehension system and visualization system. The music signal is subjected to a short-time Fourier transform to obtain the frequency spectrum. According to the perception characteristics of the human auditory system, the spectrum amplitude is logarithmically processed, and the endpoint intensity curve and the phase information of the peak value are output through half-wave rectification. The Pulse Code Modulation (PCM) characteristic value is extracted according to the autocorrelation characteristic of the endpoint intensity curve. This article proposes a rhythm detection algorithm based on multipath search and cluster analysis; that is, based on the clustering algorithm, it absorbs the idea of multipath tracking and proposes its own detection and tracking algorithm. It overcomes the weakness of the clustering algorithm that needs to use Musical Instrument Digital Interface (MIDI) auxiliary input to achieve the desired effect. This algorithm completely uses the PCM signal as the input, which is more robust than the clustering algorithm. The whole process is carried out in the time domain, and the amount of calculation is much smaller than the frequency domain calculation of multipath tracking, and the linear relationship with the rhythm of the music is much better than the filter bank algorithm. This algorithm can successfully detect the rhythm of the music with a strong sense of rhythm and can track the specific position of the rhythm point.

In this paper, a simple image compression algorithm is proposed for wireless capsule endoscopy. The proposed algorithm consists of new simplified YUV colour space, corner clipping, uniform quantization, subsampling, differential pulse code modulation and Golomb Rice code. Simplified YUV colour space is proposed based on special nature of endoscopic images and provide good results. The quantization and subsampling are used as lossy compression techniques and fixed Golomb-Rice code is used to encode residual value obtained after differential pulse code modulation operation. Here performance of different combination of quantization and subsampling techniques are analyzed based combination along with the proposed compression algorithm provides compression ratio of 89.3% and peak signal noise ratio of 45.1. the proposed algorithm provided better results as compared to various reported algorithms in literature in term of CR and PSNR.

In this paper, a new high-performance lossy compression technique based on DCT is proposed. The image is partitioned into blocks of a size of NxN (where N is multiple of 2), each block is categorized whether it is high frequency (uncorrelated block) or low frequency (correlated block) according to its spatial details, this done by calculating the energy of block by taking the absolute sum of differential pulse code modulation (DPCM) differences between pixels to determine the level of correlation by using a specified threshold value. The image blocks will be scanned and converted into 1D vectors using horizontal scan order. Then, 1D-DCT is applied for each vector to produce transform coefficients. The transformed coefficients will be quantized with different quantization values according to the energy of the block. Finally, an enhanced entropy encoder technique is applied to store the quantized coefficients. To test the level of compression, the quantitative measures of the peak signal-to-noise ratio (PSNR) and compression ratio (CR) is used to ensure the effectiveness of the suggested system. The PSNR values of the reconstructed images are taken between the intermediate range from 28dB to 40dB, the best attained compression gain on standard Lena image has been increased to be around (96.60 %). Also, the results were compared to those of the standard JPEG system utilized in the “ACDSee Ultimate 2020” software to evaluate the performance of the proposed system.

Due to unavoidable environmental factors, wireless sensor networks are facing numerous tribulations regarding network coverage. These arose due to the uncouth deployment of the sensor nodes in the wireless coverage area that ultimately degrades the performance and confines the coverage range. In order to enhance the network coverage range, an instance (node) redeployment-based Bodacious-instance Coverage Mechanism (BiCM) is proposed. The proposed mechanism creates new instance positions in the coverage area. It operates in two stages; in the first stage, it locates the intended instance position through the Dissimilitude Enhancement Scheme (DES) and moves the instance to a new position, while the second stage is called the depuration, when the moving distance between the initial and intended instance positions is sagaciously reduced. Further, the variations of various parameters of BiCM such as loudness, pulse emission rate, maximum frequency, grid points, and sensing radius have been explored, and the optimized parameters are identified. The performance metric has been meticulously analyzed through simulation results and is compared with the state-of-the-art Fruit Fly Optimization Algorithm (FOA) and, one step above, the tuned BiCM algorithm in terms of mean coverage rate, computation time, and standard deviation. The coverage range curve for various numbers of iterations and sensor nodes is also presented for the tuned Bodacious-instance Coverage Mechanism (tuned BiCM), BiCM, and FOA. The performance metrics generated by the simulation have vouched for the effectiveness of tuned BiCM as it achieved more coverage range than BiCM and FOA.

In today’s world of information exchange, digital imaging of pathological information has grown very fast and hence plays an essential role in disease diagnosis. Radiological modalities such as Computed Tomography (CT) scanners, Magnetic Resonance Imaging (MRI), X-Ray are increasing massively. The demand for handling images in digital form has increased dramatically in recent years for efficient archiving and transmission that raises the need of efficient compression. In this paper, a near-lossless predictive based coding technique is proposed that address the low coding efficiency of lossless coding and low image quality of lossy coding. The proposed technique removes inter-pixel redundancy and physco-visual redundancy by employing Resolution Independent Gradient Edge Detector (RIGED) at optimal threshold value and optimal q-level for quantization respectively. Coding redundancy is removed by employing block based encoding at optimal fixed size block. Volumetric MR image datasets of 8 bit and 16 bit are utilized for implementation of the proposed technique. The proposed technique is compared with the state-of-the art techniques and outperforms Context Adaptive Lossless Image Compression (CALIC), Low Complexity Lossless Compression for Images (LOCO-I) and Differential Pulse Code Modulation (DPCM) with context switching NN predictor by 34.33%, 39.80% and 30.35% respectively.

Compressive Video Sensing (CVS) is the video coding framework that uses compressive sensing (CS) theory into video codec to reduce the burden of encoding. Various existing methods are developed for CVS, but enhancing reconstruction speed and reconstruction quality remains a great challenge in video compression. Hence, an optimal method is developed using the proposed Differential Pulse Code Modulation+Conditional Autoregressive-Salp Swarm (DPCM+CA-SS) model for CVS system by considering the characteristics of CVS frame. The spatial redundancy of video frames is explored by partitioning the video frames into several blocks such that each block is measured using the measurement matrix. The measurement vectors are quantized using DPCM. The key frames are compressed into bits using Huffman coding, and the packet is transmitted to the decoder side. The temporal redundancy of CVS model is explored by encoding the frames using CA-SS-based prediction model to achieve Motion Aligned (MA) optimal reconstruction. However, the proposed CA-SS is designed by the integration of Conditional Autoregressive Value at Risk (CAViaR) and Salp Swarm Algorithm (SSA), respectively. However, the proposed DPCM+CA-SS obtained maximum PSNR and SSIM of 39.6649 db and 0.9120, and the minimum total time of 4.9523 s, respectively.

The lossless intra-prediction coding modality of the High Efficiency Video Coding standard provides high coding performance while allowing frame-by-frame basis access to the coded data. This is of interest in many professional applications, such as medical imaging, automotive vision, and digital preservation in libraries and archives. Various improvements to lossless intra-prediction coding have been proposed recently, most of them based on sample-wise prediction using differential pulse code modulation (DPCM). Other recent proposals aim at further reducing the energy of intra-predicted residual blocks. However, the energy reduction achieved is frequently minimal due to the difficulty of correctly predicting the sign and magnitude of residual values. In this paper, we pursue a novel approach to this energy-reduction problem using piecewise mapping (pwm) functions. In particular, we analyze the range of values in residual blocks and apply accordingly a pwm function to map specific residual values to unique lower values. We encode the appropriate parameters associated with the pwm functions at the encoder, so that the corresponding inverse pwm functions at the decoder can map values back to the same residual values. These residual values are then used to reconstruct the original signal. This mapping is, therefore, reversible and introduces no losses. We evaluate the pwm functions on $4\times 4$ residual blocks computed after DPCM-based prediction for lossless coding of a variety of camera-captured and screen content sequences. Evaluation results show that the pwm functions can attain the maximum bit-rate reductions of 5.54% and 28.33% for screen content material compared with DPCM-based and block-wise intra-prediction, respectively. Compared with intra-block copy, piecewise mapping can attain the maximum bit-rate reductions of 11.48% for a camera-captured material.

A compression method based on non-uniform binary scalar quantization, designed for the memoryless Laplacian source with zero-mean and unit variance, is analyzed in this paper. Two quantizer design approaches are presented that investigate the effect of clipping with the aim of reducing the quantization noise, where the minimal mean-squared error distortion is used to determine the optimal clipping factor. A detailed comparison of both models is provided, and the performance evaluation in a wide dynamic range of input data variances is also performed. The observed binary scalar quantization models are applied in standard signal processing tasks, such as speech and image quantization, but also to quantization of neural network parameters. The motivation behind the binary quantization of neural network weights is the model compression by a factor of 32, which is crucial for implementation in mobile or embedded devices with limited memory and processing power. The experimental results follow well the theoretical models, confirming their applicability in real-world applications.

In this paper, we introduce the zero-delay multiple-description problem, where an encoder constructs two descriptions and the decoders receive a subset of these descriptions. The encoder and decoders are causal and operate under the restriction of zero delay, which implies that at each time instance, the encoder must generate codewords that can be decoded by the decoders using only the current and past codewords. For the case of discrete-time stationary scalar Gauss&mdash, Markov sources and quadratic distortion constraints, we present information-theoretic lower bounds on the average sum-rate in terms of the directed and mutual information rate between the source and the decoder reproductions. Furthermore, we show that the optimum test channel is in this case Gaussian, and it can be realized by a feedback coding scheme that utilizes prediction and correlated Gaussian noises. Operational achievable results are considered in the high-rate scenario using a simple differential pulse code modulation scheme with staggered quantizers. Using this scheme, we achieve operational rates within 0 . 415 bits / sample / description of the theoretical lower bounds for varying description rates.

Paired carrier multiple access (PCMA) is one of the most common single-channel mixtures. It is still a great challenge to recover the transmitted bits from non-cooperative received PCMA signals, due to the high complexity of existing single-channel blind source separation (SCBSS) algorithms. Hence, a double-direction delayed-decision-feedback sequence estimation (DD-DDFSE) algorithm for non-causal high-order channel is proposed to realise the separation of PCMA signals. The proposed algorithm employs the Viterbi algorithm (VA) with low-order channel twice instead of conventional VA with high-order channel once. The symbols estimated by the initial VA are utilised to equalise the causal and non-causal taps of the channel beyond the trellis state in the second VA. The relationship between the decision-feedback error from the initial VA and the performance of the second VA is also derived. Compared with state-of-the-art maximum likelihood sequence estimation algorithms, DD-DDFSE algorithm can not only decrease the computation complexity but also improve the separation performance.

This paper presents new detectors developed to handle the severe nonlinear distortion introduced by 32 kb/s Adaptive Differential Pulse Code Modulation (ADPCM). These detectors are tested against previously developed detector named system-B using data transmission at 9.6 kb/s over ADPCM link in cascade with telephone channel. Simulation results show that the error rates of the proposed detectors approach negligible values at certain signal-to-noise-ratio (SNR), while, the error rate of system-B remains constant at certain value.

In this paper, a simple color image compression system has been proposed using image signal decomposition. Where, the RGB image color band is converted to the less correlated YUV color model and the pixel value (magnitude) in each band is decomposed into 2-values; most and least significant. According to the importance of the most significant value (MSV) that influenced by any simply modification happened, an adaptive lossless image compression system is proposed using bit plane (BP) slicing, delta pulse code modulation (Delta PCM), adaptive quadtree (QT) partitioning followed by an adaptive shift encoder. On the other hand, a lossy compression system is introduced to handle the least significant value (LSV), it is based on an adaptive, error bounded coding system, and it uses the DCT compression scheme. The performance of the developed compression system was analyzed and compared with those attained from the universal standard JPEG, and the results of applying the proposed system indicated its performance is comparable or better than that of the JPEG standards.

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 7.1311.41dB (or equivalently 51.2366.96% bitrate savings) compared with four existing video coding solutions, which also have low computational complexity and suit for the CSV system. A new efficient coding framework for compressive sensing video (CSV) is proposed.The proposed framework is based on GMM and product vector quantizer.The proposed framework achieves a significant coding rate-distortion improvement.The computational complexity of the proposed CSV encoding algorithm is low.

It is well known that aurorae have very high research value, but the data volume of aurora spectral data is very large, which brings great challenges to storage and transmission. To alleviate this problem, compression of aurora spectral data is indispensable. This paper presents a parallel Compute Unified Device Architecture (CUDA) implementation of the prediction-based online Differential Pulse Code Modulation (DPCM) method for the lossless compression of the aurora spectral data. Two improvements are proposed to improve the compression performance of the online DPCM method. One is on the computing of the prediction coefficients, and the other is on the encoding of the residual. In the CUDA implementation, we proposed a decomposition method for the matrix multiplication to avoid redundant data accesses and calculations. In addition, the CUDA implementation is optimized with a multi-stream technique and multi-graphics processing unit (GPU) technique, respectively. Finally, the average compression time of an aurora spectral image reaches about 0.06 s, which is much less than the 15 s aurora spectral data acquisition time interval and can save a lot of time for transmission and other subsequent tasks.

In this paper, new parameterized differential pulse-code modulation interpolator is proposed. It uses the correspondence of signal samples in the processed sample vicinity to select the interpolation method. Interpolation error of proposed interpolator is minimized during the optimization procedure. Before the optimization, the parameter range dimension is reduced in order to lower the computational complexity. Experimental research shows that proposed interpolator outperforms the non-parameterized one

High Efficiency Video Coding (HEVC) adopts intra transform skip mode, in which a residual block is directly quantized in the pixel domain without transforming the block into the frequency domain. Intra transform skip mode provides a significant coding gain for screen content. However, when intra-prediction errors are not transformed, the errors are often correlated along the intra-prediction direction. This paper introduces a residual differential pulse code modulation (DPCM) method for the intra-predicted and transform-skipped blocks to remove redundancy. The proposed method performs pixel-by-pixel residual prediction along the intra-prediction direction to reduce the dynamic range of intra-prediction errors. Experimental results show that the transform skip mode’s Bjontegaard delta rate (BD-rate) is improved by 12.8% for vertically intra-predicted blocks. Overall, the proposed method shows an average 1.2% reduction in BD-rate, relative to HEVC, with negligible computational complexity.

Parameterization of the nonlinear Greham predictor is performed for a digital image compression method based on the differential pulse code modulation. The predictor automatically selects different methods for calculating each pixel in the image based on the availability and intensity of that pixel contour. A fast learning procedure that optimizes the prediction parameters is performed before the actual compression. In the course of optimization, the minimum sum of absolute values of prediction errors is provided. For this purpose, a recursive procedure is used, whose computational complexity is independent of the image size. The estimation of the computational complexity of the proposed predictor is conducted. To study the predictors computational experiments are carried out on real images. A gain the proposed predictor offers in terms of the root mean square error when compared with the prototypes is demonstrated. In addition, a gain that the compression method based on the differential pulse code modulation with the proposed predictor has over the JPEG compression method in terms of the maximum error is demonstrated.

Presented a low cost technique for transmitting data through radio frequency antenna using low power, which is the combination of low computational complexity image compression algorithm and simple colour reproduction algorithm. In wireless capsule endoscopy, radio frequency antenna consume more than 60% of power for transmitting data and transmitting all the RGB components consume more power. In the proposed scheme, the first compressed colour frame is transmitted followed by few number of compressed grey frames. Compression algorithm is made of simplified YUV colour space, differential pulse code modulation and Golomb Rice code. At decoder dictionary based colour reproduction algorithm is used to colorize the grey scale image using colour information of original colour image. The proposed scheme lead to saving of more than 50% power of the battery, which increases the battery life from 10 h to 18 h. This extra battery life can be used for adding extra features to capsule, which help in development of diagnosing and treatment capsules.

This paper presents a quality controlled reconstruction of ECG signal by the formulation of 2D Discrete Cosine Transform (DCT) coefficient and iterative JPEG2000 encoding scheme for Electrocardiogram (ECG) data compression. It employs variable blockwise (L = 32 to 2048 samples) DCT to exploit inter and intra-beat correlation inherent in ECG. The Regula-Falsi method is applied to obtain the optimal threshold for the DCT coefficients, which are stacked row-wise in order to form a 2D DCT coefficient array for the correlation analysis. Afterwards, window-based filtering is applied to retain the significant DCT coefficients with varying window size (ws). Differential pulse code modulation based quantization scheme is then applied to 2D DCT coefficients array in order to make it conformal to JPEG2000 encoding. Finally, iterative JPEG2000 encoding is utilized for achieving the overall predefined reconstruction error. The experimentations were performed on all the 48 records of Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. The proposed method provides compression ratio = 39.48 & 40.18; percentage root mean square difference = 2.02 & 2.06 and the quality score = 19.45 & 19.56 at ws = 50% & 20% respectively for L = 256. The proposed technique results were also validated on all records of four standard ECG databases including i) MIT-BIH Normal sinus rhythm ii) CU ventricular tachyarrhythmia iii) CSE and iv) PTB Database. Further, a comparative analysis between the proposed method and some recent existing works was also performed. The results clearly demonstrate the superiority of proposed method.

Compressed sensing (CS) have gained much interest in recent years for its advantage of simultaneously acquiring and compressing signals. The acquired signals using CS need quantization to be exploitable in digital systems for storage or transmission. Split vector quantization (SVQ) is proposed to quantize the CS measurements, and a speech compression application is performed to illustrate its usefulness. The proposed method is compared with state-of-the-art quantization techniques which are scalar quantization, differential pulse code modulation, vector quantization, and multistage vector quantization for speech compression in terms of perceptual evaluation of speech quality, signal-to-noise ratio, mean square error, and execution time. Results demonstrate that SVQ represents a competitive alternative of the studied methods for CS measurements quantization.

In this paper, a parameterized four direction contour-invariant extrapolator for differential pulse code modulation (DPCM) image compression is presented. Two calculation methods for extrapolation are available - linear and nonlinear one. Chosen method for extrapolation depends on the presence of a contour inside the vicinity of every processed pixel. Parameter responsible for the choice of calculation method is optimized before compression during recursive image size independent learning procedure. Computational experiments are carried out on a test set of images in order to examine proposed extrapolator. The advantage in terms of RMS error of proposed extrapolator over other ones on a certain types of images is shown.

This paper proposes a novel one-dimensional (1-D) signal compression technique. We first perform beat-alignment to transform a 1-D signal into 2-D, then use 2-D discrete wavelet transform (DWT) to further decompose the 2-D signal into multiple subbands. These coefficients in certain subbands are then coded using a simple differential pulse code modulation (DPCM). After which, we construct neural networks one for each subband (except the LL subband) to perform prediction. Based on the prediction results, we construct a type of pixel-wise context A to determine the activity of a given pixel. At last, the DWT coefficients and residues from DPCM are bit-plane coded using the Embedded Block Coding with Optimized Truncation (EBCOT) from JPEG2000. We analyzed our results using a well- known 1D signal, the ECG signals in the MIT-BIH database, and it demonstrated significant improvement over existing methods.

An image compressor inside wireless capsule endoscope should have low power consumption, small silicon area, high compression rate and high reconstructed image quality. Simple and efficient image compression scheme, consisting of reversible color space transformation, quantization, subsampling, differential pulse code modulation (DPCM) and Golomb---Rice encoding, is presented in this paper. To optimize these methods and combine them optimally, the unique properties of human gastrointestinal tract image are exploited. Computationally simple and suitable color spaces for efficient compression of gastrointestinal tract images are proposed. Quantization and subsampling methods are optimally combined. A hardware-efficient, locally adaptive, Golomb---Rice entropy encoder is employed. The proposed image compression scheme gives an average compression rate of 90.35 % and peak signal-to-noise ratio of 40.66 dB. ASIC has been fabricated on UMC130nm CMOS process using Faraday high-speed standard cell library. The core of the chip occupies 0.018 mm$$^2$$2 and consumes 35 $$\upmu {\text {W}}$$μW power. The experiment was performed at 2 frames per second on a $$256\times 256$$256?256 color image. The power consumption is further reduced from 35 to 9.66 $$\upmu $$μW by implementing the proposed image compression scheme using Faraday low-leakage standard cell library on UMC130nm process. As compared to the existing DPCM-based implementations, our realization achieves a significantly higher compression rate for similar area and power consumption. We achieve almost as high compression rate as can be achieved with existing DCT-based image compression methods, but with an order of reduced area and power consumption.

Characteristics of PCM(Pulse Code Modulation) waveform are suitable for target tracking. Especially in terms of dwell time, it is desirable to detect and track a moving target with the single PCM waveform for a MFR(Multi-Function Radar) which carries out multiple tasks. General PCM waveform processing includes Doppler filter bank caused by the characteristics of ambiguity function, to detect target and estimate Doppler frequency, which induces hardware burden and computational complexity. We propose a ML(Maximum Likelihood) based Doppler estimator for a PCM waveform, which is the closed form suboptimal solution and computationally efficient to estimate Doppler frequency and detect a moving target. Key words: Pulse Code Modulation Waveform, Doppler Frequency Estimation, Multi-Function Radar, Target Detection h\Tijkl(Agency for Defense Development)Manuscript received August 26, 2014 ; Revised October 15, 2014 ; Accepted November 4, 2014. (ID No. 20140826-063)Corresponding Author: Eunjung Yang (e-mail: ejyang@add.re.kr)

Video coding standards are primarily designed for efficient lossy compression, but it is also desirable to support efficient lossless compression within video coding standards using small modifications to the lossy coding architecture. A simple approach is to skip transform and quantization, and simply entropy code the prediction residual. However, this approach is inefficient at compression. A more efficient and popular approach is to skip transform and quantization but also process the residual block with DPCM, along the horizontal or vertical direction, prior to entropy coding. This paper explores an alternative approach based on processing the residual block with integer-to-integer (i2i) transforms. I2i transforms can map integer pixels to integer transform coefficients without increasing the dynamic range and can be used for lossless compression. We focus on lossless intra coding and develop novel i2i approximations of the odd type-3 DST (ODST-3). Experimental results with the HEVC reference software show that the developed i2i approximations of the ODST-3 improve lossless intra-frame compression efficiency with respect to HEVC version 2, which uses the popular DPCM method, by an average 2.7% without a significant effect on computational complexity., Draft consisting of 16 pages

The adaptive nonlinear predictor is proposed for digital image compression method based on differential pulse code modulation. Greham predictor is parameterized for this. Proposed predictor works in different ways depending on the local image contours. A special feature is offered for estimation the contour direction and intensity in the neighborhood of the current pixel. The parameters of the proposed predictor are calculated by rapid training procedure before the actual compression. This procedure minimizes the sum of absolute values of prediction errors. Theoretical computational complexity of the proposed predictor is shown. Considered predictors are compared in real images by computational experiments. The win of proposed algorithm is demonstrated. In addition, the gain of compression method based on differential pulse code modulation with the proposed predictor against JPEG compression method is demonstrated.

In this paper we present a video compression algorithm based on predictive vector quantization, which is a combination of vector quantization and differential pulse code modulation. We optimized the algorithm using chroma subsampling which reduces the amount of information that needs to be processed. This allowed us to combine two color channels into one and thereby reduce the number of predictors and codebooks. Furthermore, we introduced inter-frames which only store regions that changed compared to previous frames, further decreasing the size of compressed data.

H.264 is well known compression standard for raw video. Data hiding using general techniques will not sustain under this scheme. We propose a data hiding approach in IPCM block. Where these IPCM encoded macro blocks are modified in order to hide the desired data. In general video, the numbers of IPCM blocks generated are very less. A method is proposed to increase the number of IPCM blocks generated in H.264 algorithm, by which more data can be hidden. It is a blind data hiding scheme, i.e. the encoded message can be extracted directly without the need of the original host video.

A new class of cascade digital orthogonal filters of the Malmquist type based on bilinear transformation for mapping poles to zeroes and vice versa is presented in this paper. In a way, it is a generalization of the majority of the classical orthogonal filters and some newly designed filters as well. These filters are orthogonal with respect to the generalized inner product which is actually a generalization of the classical inner product. Outputs of these filters are obtained by using polynomials orthogonal with respect to the new inner product. The main quality of these filters is that they are parametric adaptive. The filter with six sections is practically realized in the Laboratory for Modeling, Simulation and Control Systems. Performances of the designed filter are proved on modeling and identification of the system for differential pulse code modulation. Real response and response from the proposed filter are compared with regard to the chosen criteria function. Also, a comparative analysis of the proposed filter with some existing filters is performed.

This paper addresses the problem of electrocardiogram (ECG) signal compression with the goal to provide a simple compression method that outperforms previously proposed methods. Starting with the study of the ECG signal nature, the manner has been found to optimize rate-quality ratio of the ECG signal by means of differential pulse code modulation (DPCM) and subframe after subframe procession. Particularly, the proposed method includes two kinds of adaptations, short-time and long-time adaptations. The switched quantization i.e. the short-time DPCM quantizer range adaptation is performed according to the statistics of the ECG signal within particular subframes. It is ascertained that the short-time adaptation enables a sophisticated compression control as well as a constant quality of the ECG signal in both segments of low amplitude and high amplitude dynamics. In addition, by grouping the subframes of a particular frame into two groups according to their dynamics and performing the long-time DPCM quantizer range adaptation, based on the statistics of the groups, it has been revealed that an important quality gain is achieved with an insignificant rate increase. Moreover, the two iterative approaches proposed in the paper, mainly differ in the fact whether the long-time range adaptations of the used DPCM quantizers are performed according to the maximum amplitudes or according to the average powers of the signal difference determined in all subframes within a certain group. The benefits of both approaches to the above proposed method are shown and discussed in the paper.

A hybrid method of block truncation coding (BTC) and differential pulse code modulation (DPCM) offers better visual quality than the standard BTC for small block sizes due to its inherent multitone representation. Recently, a two-level quantizer design method has been proposed to increase the coding performance of the DPCM-BTC framework. However, the design method is near optimal in the sense that its coding performance depends on the initial bit plane patterns. In this paper, we propose a bit plane modification (BPM) algorithm to achieve further performance improvement. The BPM algorithm, inspired by error diffusion, effectively distributes large quantization error at a certain pixel to its neighboring pixels having small quantization errors by changing partial bit patterns. Experimental results show that the proposed algorithm successfully achieves much higher coding performance than various conventional BTC methods. The average PSNR performance of the proposed method is 2.31 dB, 5.15 dB, and 5.15 dB higher than that of BTC, DPCM-BTC, and a recently developed BTC scheme using error diffusion and bilateral filtering, respectively.

Large images consume more storage space needing high data rates for transmission demanding the innovation of efficient image compression systems. Owing to the massive parallel architecture and generalization ability of neural networks to memorize inputs even on untrained data, the computational simplicity of wavelets, ability of Differential Pulse Code Modulation (DPCM) to reduce the unused or redundant bits in the information, in this paper an hybrid image compression system combining the advantages of wavelets and neural networks is implemented along with Differential Pulse Code Modulation based on the predicted sample values. Scalar quantization and Huffman encoding schemes are used as well for compressing different sub bands i.e the low frequency band coefficients are compressed by the DPCM while the high frequency band coefficients are compressed using neural networks. Satisfactory reconstructed images with increased bit rates and large Peak Signal to Noise Ratio (PSNR) can be achieved with this scheme. Wavelet transform eliminates the blocking artefacts’ associated with cosine transform and neural networks minimize the Mean Square Error (MSE).Empirical analysis and metrics calculation is performed for the sake of relative analysis. General Terms Artificial Neural Networks, Fidelity, Mean Square Error, Peak Signal to Noise Ratio, Quantization and Huffman encoding.

Representing a continuous-time signal by a set of samples is a classical problem in signal processing. We study this problem under the additional constraint that the samples are quantized or compressed in a lossy manner under a limited bitrate budget. To this end, we consider a combined sampling and source coding problem in which an analog stationary Gaussian signal is reconstructed from its encoded samples. These samples are obtained by a set of bounded linear functionals of the continuous-time path, with a limitation on the average number of samples obtained per unit time available in this setting. We provide a full characterization of the minimal distortion in terms of the sampling frequency, the bitrate, and the signal's spectrum. Assuming that the signal's energy is not uniformly distributed over its spectral support, we show that for each compression bitrate there exists a critical sampling frequency smaller than the Nyquist rate, such that the distortion in signal reconstruction when sampling at this frequency is minimal. Our results can be seen as an extension of the classical sampling theorem for bandlimited random processes in the sense that it describes the minimal amount of excess distortion in the reconstruction due to lossy compression of the samples, and provides the minimal sampling frequency required in order to achieve this distortion. Finally, we compare the fundamental limits in the combined source coding and sampling problem to the performance of pulse code modulation (PCM), where each sample is quantized by a scalar quantizer using a fixed number of bits., Comment: 20 pages, 14 figures

In the last few decades, Digital image compression has received significant attention of researchers. Recently, based on wavelets there has been many compression algorithms. In comparison to other compression techniques, image compression using wavelet based algorithms lead to high compression ratios. In this paper, we have proposed a image compression algorithm which combines the feature of both wavelet transform and Radial Basis Function Neural Network along with vector quantization. First the images are decomposed into a set of subbands having different resolution with respect to different frequency bands using wavelet filters. Based on their statistical properties, different coding and quantization techniques are employed. The Differential Pulse Code Modulation (DPCM) is used to compress the low frequency band coefficients and Radial Basis Function Neural Network (RBFNN) is used to compress the high frequency band coefficients. The hidden layer coefficients of RBFNN subsequently are vector quantized so that without much degradation of the reconstructed image, the compression ratio can be increased. In terms of peak signal to noise ratio (PSNR) and computation time (CT), a large compression ratio has been achieved with satisfactory reconstructed images in relation to the existing methods by using the proposed technique.

This paper presents the architecture, algorithm, and VLSI hardware of image acquisition, storage, and compression on a single-chip CMOS image sensor. The image array is based on time domain digital pixel sensor technology equipped with nondestructive storage capability using 8-bit Static-RAM device embedded at the pixel level. The pixel-level memory is used to store the uncompressed illumination data during the integration mode as well as the compressed illumination data obtained after the compression stage. An adaptive quantization scheme based on fast boundary adaptation rule (FBAR) and differential pulse code modulation (DPCM) procedure followed by an online, least storage quadrant tree decomposition (QTD) processing is proposed enabling a robust and compact image compression processor. A prototype chip including 64×64 pixels, read-out and control circuitry as well as an on-chip compression processor was implemented in 0.35 μm CMOS technology with a silicon area of 3.2×3.0 mm2 and an overall power of 17 mW. Simulation and measurements results show compression figures corresponding to 0.6-1 bit-per-pixel (BPP), while maintaining reasonable peak signal-to-noise ratio levels.

A two-dimensional (2-D) quantization scheme for the digitized radio-over-fiber (DRoF) transmission system is proposed and experimentally demonstrated. By using a self-organizing feature map (SOFM) neural network clustering method to implement the 2-D quantization, the spectral efficiency of the DRoF system can be effectively improved, and a better transmission performance is achieved. Compared with the conventional scalar quantization based on pulse code modulation in the common public radio interface, the sampled sequence is constructed as a 2-D array, and the SOFM neural network clustering method is employed to obtain an optimized quantization codebook, which can achieve a better error vector magnitude performance with the same quantization bits. In the experiments, the proposed scheme for the DRoF transmission is demonstrated in a 25 km 5 Gbaud/λ four-level pulse amplitude modulation (PAM-4) intensity modulation and direct detection optical link. For the 5G NR standard, carrier aggregations of 27, 20, 16, and 13 100 MHz orthogonal frequency division multiplexing with 4, 16, 64, and 256 QAM at quantization bits per sample of 3, 4, 5, and 6 are successfully achieved, respectively.

In this paper, a new design paradigm referred to as compressive acquisition CMOS image sensors is introduced. The idea consists of compressing the data within each pixel prior to storage, and hence, reducing the size of the memory required for digital pixel sensor. The proposed compression algorithm uses a block-based differential coding scheme in which differential values are captured and quantized online. A time-domain encoding scheme is used in our CMOS image sensor in which the brightest pixel within each block fires first and is selected as the reference pixel. The differential values between subsequent pixels and the reference within each block are calculated and quantized, using a reduced number of bits as their dynamic range is compressed. The proposed scheme enables reduced error accumulation as full precision is used at the start of each block, while also enabling reduced memory requirement, and hence, enabling significant silicon area saving. A mathematical model is derived to analyze the performance of the algorithm. Experimental results on a field-programmable gate-array (FPGA) platform illustrate that the proposed algorithm enables more than 50% memory saving at a peak signal-to-noise ratio level of 30 dB with 1.5 bit per pixel.

A quantitative comparison of Pulse Code Modulation (PCM) and Sigma- Delta (ΣΔ) quantization methods is made in the setting of finite frames. Frames allow for redundant, overcomplete signal decompositions. PCM and ΣΔ are two industry-standard quantization methods, and the setting of finite frames is appropriate for a host of modern applications. Previous results for this comparison are known for upper error bounds, where ΣΔ performs better in the setting of frames, as opposed to orthonormal bases, where PCM is optimal. We answer the following question: For which signals x is the PCM error, that is, the norm of the difference between x and its PCM approximant, less than the ΣΔerror? We prove that, typically, in the setting of frames, ΣΔoutperforms PCM, but not always.

Spherical logarithmic quantization (SLQ) is a vector quantization method for efficiently digitizing analog signals at a high dynamic range and with very low distortion while preserving the original waveform as closely as possible. SLQ is able to operate at a low data rate of, e.g., 2 bits per sample and at a very low signal delay of about ten samples, this corresponds to approximately 200 mus for high-quality audio signals. The technique of SLQ is universally applicable (i.e., not restricted to, e.g., audio signals) and achieves an efficient digital representation of waveforms with high longterm as well as high segmental signal-to-noise ratios. The aim of this paper is to give a detailed description of the SLQ algorithm and to present simulation results on the performance of this new quantization scheme that combines several advantages. After a review of some important basic principles concerning quantization, linear prediction and multidimensional spheres, the SLQ encoder is described. To short vectors of signal samples which are represented in sphere coordinates, logarithmic quantization is applied to the radius and uniform quantization is applied to the angles. This results in the advantage of a constant signal-to-noise ratio over a very high dynamic range at a small loss with respect to the rate-distortion theory. In order to increase the signal-to-noise ratio by exploitation of correlations within the source signal, a solution for the problem of combining this vector quantization scheme with scalar adaptive differential pulse code modulation (ADPCM), i.e., ADPCM with sample by sample backward recursion is presented. Furthermore, an indexing scheme for the quantization cells covering the surface of a multidimensional unit sphere is presented and simulation results using different source signals are given.

Vibration-based machine health monitoring is a well-known technique that is commonly used in machine fault diagnosis applications. In conjunction with existing wireless technologies, even machines that are operated in a hazardous or moveable environment can be monitored wirelessly. However, to facilitate a reliable machine health assessment, various sensory data, particularly the vibration signals, must be acquired at a high sampling rate, generating large packets of data. These requirements are impractical in a wireless data transmission context due to the usual low transmission rate, and lengthy transmission time. One possible solution is to compress the captured numerous sensory data before wireless transmission commences. This paper presents a novel data compression algorithm that combines Empirical Mode Decomposition (EMD) with Differential Pulse Code Modulation (DPCM). EMD is effective in decomposing and identifying any instantaneous changes in non-linear, and non-stationary signals that are caused by anomalous operation of machines. After the data have been decomposed and compressed by EMD, the DPCM is applied to further compress the data through the use of linear predictor and quantizer prior to wireless transmission. A pair of Bluetooth based wireless devices was tailor-made to host the EMD and DPCM for performing wireless communication. The effectiveness of the new algorithm has been verified using real machines, operating in a noisy environment. The results prove that the algorithm provides much faster wireless data transmission by significantly reducing the size of sensory data. Nevertheless, it maintains the integrity of sensory data with negligible reconstruction error at the receiving end of the wireless device.

The paper addresses a bitstream scalable coder based on the MPEG-4 scalable lossless (SLS) coding system where, in contrast to SLS, the bitrate of the enhancement layer is not fixed but instead an attempt is made to create a quality-fixed enhancement layer. With a PCM audio input, the proposed structure is able to produce an audio version with near-transparent quality on top of the existing low-quality version. In particular, the proposed fixed quality enhancing process with checking procedures is able to provide the minimum amount of enhancement for the low-quality version to obtain a near-transparent quality that is almost indistinguishable from the CD quality. In addition, a bitrate estimation model is proposed. The model enables the direct estimation of the enhancing bitrate from two parameters extracted from the encoding process of the low-quality version. Evaluation results indicate that a better defined quality level is guaranteed compared to a fixed bitrate setting and that in the mean a lower (approximately 20%) bitrate is attained. It is also shown that the estimation model proposed is able to accurately predict the necessary enhancing bitrate and at the same time, reduce the complexity by around 17%.

In this letter, a new Inter lossless coding method based on a residual DPCM (Differential Pulse Code Modulation) is proposed to improve compression ratio in the H.264 standard. Since the spatial correlation in a residual block can be further exploited among the residual signals after motion estimation/compensation, horizontal or vertical DPCM in the residual signals can be applied to further reduce the magnitudes of the residual signals. The proposed method reduces the average bitrates of 3.5% compared with the Inter lossless coding of the H.264 standard.

The iterative receivers with a list sphere detector (LSD) and a parallel interference canceller (PIC) for multiple-input-multiple-output (MIMO) bit-interleaved coded modulation systems are considered. The convergence behaviors of these iterative detection and decoding schemes are analyzed via variance transfer (VT) functions. For ergodic channels, we show that the waterfall region of an iterative receiver can be predicted using a variance exchange graph. For slow fading channels, we show that the frame error rate of the iterative receiver is essentially limited by the early interception rate of the iterative receiver. Moreover, we show that the VT function of an LSD with a small list size is superior to that of a PIC at a high-variance region, whereas it is worse than that of a PIC at a low-variance region. Then, we propose a detection switching (DSW) approach for the iterative receiver and present a DSW criterion based on cross entropy. By applying the DSW, we show that the near-capacity performance can be achieved with a significantly reduced complexity.

Differential pulse code modulation (DPCM) is able to code highly correlated sources efficiently at high bit rates but not at low bit-rate regions. Motivated by the rate-distortion theory, a simple modified DPCM codec using multirate processing is proposed. A low-pass filter is used to limit the source signal spectrum according to the optimal mapping scheme. Bit rate reduction is achieved by reducing the number of transmitted samples through downsampling. A Wiener filter is appended at the decoder for further noise reduction. Simulation results agree with theoretical analysis and show that for a first-order autoregressive [AR(1)] process, the proposed codec makes substantial improvements over the classic DPCM and performs close to the rate-distortion bound. The proposed system is then implemented as a low-rate predictive image coder. In addition to apply two-dimensional DPCM, adaptive entropy coding is used to exploit variation in the local statistics of the prediction error image. Simulation results show that the system is able to outperform JPEG at low rates and has subjective quality comparable to that of JPEG2000.

In transmission, storaging and coding of digital signals we frequently perform A/D conversion using quantization. In this paper we study the maximal and mean square errors as a result of quantization. We focus on the sigma–delta modulation quantization scheme in the finite frame expansion setting. We show that this problem is related to the classical Traveling Salesman Problem (TSP) in the Euclidean space. It is known [Benedetto et al., Sigma–delta (\(\Sigma\Delta\)) quantization and finite frames, IEEE Trans. Inform. Theory 52, 1990–2005 (2006)] that the error bounds from the sigma–delta scheme depends on the ordering of the frame elements. By examining a priori bounds for the Euclidean TSP we show that error bounds in the sigma–delta scheme is superior to those from the pulse code modulation (PCM) scheme in general. We also give a recursive algorithm for finding an ordering of the frame elements that will lead to good maximal error and mean square error.

In this paper, a joint use of the discrete cosine transform (DCT), and differential pulse code modulation (DPCM) based quantization is presented for predefined quality controlled electrocardiogram (ECG) data compression. The formulated approach exploits the energy compaction property in transformed domain. The DPCM quantization has been applied to zero-sequence grouped DCT coefficients that were optimally thresholded via Regula-Falsi method. The generated sequence is encoded using Huffman coding. This encoded series is further converted to a valid ASCII code using the standard codebook for transmission purpose. Such a coded series possesses inherent encryption capability. The proposed technique is validated on all 48 records of standard MIT-BIH database using different measures for compression and encryption. The acquisition time has been taken in accordance to that existed in literature for the fair comparison with contemporary state-of-art approaches. The chosen measures are (1) compression ratio (CR), (2) percent root mean square difference (PRD), (3) percent root mean square difference without base (PRD1), (4) percent root mean square difference normalized (PRDN), (5) root mean square (RMS) error, (6) signal to noise ratio (SNR), (7) quality score (QS), (8) entropy, (9) Entropy score (ES) and (10) correlation coefficient (r x,y ). Prominently the average values of CR, PRD and QS were equal to 18.03, 1.06, and 17.57 respectively. Similarly, the mean encryption metrics i.e. entropy, ES and r x,y were 7.9692, 0.9962 and 0.0113 respectively. The novelty in combining the approaches is well justified by the values of these metrics that are significantly higher than the comparison counterparts.

The jointly optimized sampling rate and quantization precision in A/D conversion is studied. In particular, we consider a basic pulse code modulation A/D scheme in which a stationary process is sampled and quantized by a scalar quantizer. We derive an expression for the minimal mean squared error under linear estimation of the analog input from the digital output, which is also valid under sub-Nyquist sampling. This expression allows for the computation of the sampling rate that minimizes the error under a fixed bitrate at the output, which is the result of an interplay between the number of bits allocated to each sample and the distortion resulting from sampling. We illustrate the results for several examples, which demonstrate the optimality of sub-Nyquist sampling in certain cases.

Processing signals continuous in both time and amplitude with a computer requires to sample, to quantize, and to code them to obtain digital signals—discrete in both time and amplitude. The uniform sampling Nyquist condition for band-limited signals indicates that the sampling period used depends on the maximum frequency present in the signal. Moreover, by using the correct sampling period, reconstruction of the original signal from the samples is possible by the Shannon’s sinc interpolation. Practical aspects of the sampling and reconstruction are discussed when considering analog-to-digital (A/D) and digital-to-analog (D/A) converters. Digital communications was initiated with the concept of pulse code modulation (PCM) for the transmission of binary signals. PCM is a practical implementation of sampling, quantization, and coding of an analog message into a digital message. Efficient use of the radio spectrum has motivated the development of multiplexing techniques in time and in frequency. In this chapter, we highlight some of the communication techniques that relate to the sampling theory, leaving the technical details for texts in digital communications. MATLAB is used to illustrate concepts.

Conditional replenishment algorithm (CRA) was proposed to improve the quality of a hybrid 3DTV system with mixed resolution. Since CRA generates additional information which includes inter-view vectors, it is needed to efficiently encode the information. This paper proposes a method to apply differential pulse code modulation (DPCM) to the inter-view vectors so as to improve the coding efficiency of CRA.