Pulse Code Modulation Research Articles

Differential pulse code modulation and motion aligned optimal reconstruction for block-based compressive video sensing using conditional autoregressive-salp swarm algorithm

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.

Stream-Based Visually Lossless Data Compression Applying Variable Bit-Length ADPCM Encoding.

Video applications have become one of the major services in the engineering field, which are implemented by server–client systems connected via the Internet, broadcasting services for mobile devices such as smartphones and surveillance cameras for security. Recently, the majority of video encoding mechanisms to reduce the data rate are mainly lossy compression methods such as the MPEG format. However, when we consider special needs for high-speed communication such as display applications and object detection ones with high accuracy from the video stream, we need to address the encoding mechanism without any loss of pixel information, called visually lossless compression. This paper focuses on the Adaptive Differential Pulse Code Modulation (ADPCM) that encodes a data stream into a constant bit length per data element. However, the conventional ADPCM does not have any mechanism to control dynamically the encoding bit length. We propose a novel ADPCM that provides a mechanism with a variable bit-length control, called ADPCM-VBL, for the encoding/decoding mechanism. Furthermore, since we expect that the encoded data from ADPCM maintains low entropy, we expect to reduce the amount of data by applying a lossless data compression. Applying ADPCM-VBL and a lossless data compression, this paper proposes a video transfer system that controls throughput autonomously in the communication data path. Through evaluations focusing on the aspects of the encoding performance and the image quality, we confirm that the proposed mechanisms effectively work on the applications that needs visually lossless compression by encoding video stream in low latency.

Processing of Digital Signals in (+1, -1) Binary System

Digital Signal Processing in (+1, -1) system has been studied in detail. Effects of number representation on quantization and truncation of rounding have been discussed. First order and second order filters have been investigated. Differential pulse code modulation method of coding is being studied. It is found that this system is well suited for VLSI design. The hardware realization of different components of DPCM is easy and straight. The position and negative signals can be processed in a unified way. It gives better performance in comparison of two’s complement representation of conventional binary system. The effect of limit cycles, stability and round off noise in first and second order filter have been discussed too.

Music Rhythm Detection Algorithm Based on Multipath Search and Cluster Analysis

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.

Image Data Compression Based on Two Hybrids Algorithms

Image compression is a way to reduce the storage space of images. Therefore, image compression process increases the performance of such as the data transmission process. This paper aims to present a new technique to compress digital image data. In this technique, two-hybrid algorithms were used to compress the image data. The first system consists of one-dimensional discrete cosine transform (DCT), differential pulse code modulation (DPCM), and Huffman code for difference signal. The second hybrid system utilizes an expert system called Learning Automata (LA) to code the difference signal obtained from the first system. A compression ratio of about (10.8:1) was obtained from the first system. The second system provides a (20.6:1) compression ratio with non-noticeable impairment. The information loss is caused by a hybrid (DCT/DPCM) system, not by the LA system. The conclusion drawn is that using two-hybrid systems to compress the image data provides a high compression ratio. Furthermore, learning automata is preferable since it removes all the redundancy in the row data. However, in learning automata, a Huffman code is determined pixel by pixel which takes a long time.

Аdaptive combined image coding with prediction of arithmetic code volume

The problem of increasing the efficiency of coding of halftone images in the space of bit planes of differences in pixel values obtained using differential coding (DPCM – Differential pulse-code modulation) is considered. For a compact representation of DPCM pixel values, it is proposed to use a combined compression encoder that implements arithmetic coding and run-length coding. An arithmetic encoder provides high compression ratios, but has high computational complexity and significant encoding overhead. This makes it effective primarily for compressing the mean-value bit-planes of DPCM pixel values. Run-length coding is extremely simple and outperforms arithmetic coding in compressing long sequences of repetitive symbols that often occur in the upper bit planes of DPCM pixel values. For DPCM bit planes of pixel values of any image, a combination of simple run length coders and complex arithmetic coders can be selected that provides the maximum compression ratio for each bit plane and all planes in general with the least computational complexity. As a result, each image has its own effective combined encoder structure, which depends on the distribution of bits in the bit planes of the DPCM pixel values. To adapt the structure of the combined encoder to the distribution of bits in the bit planes of DPCM pixel values, the article proposes to use prediction of the volume of arithmetic code based on entropy and comparison of the obtained predicted value with the volume of run length code. The entropy is calculated based on the values of the number of repetitions of ones and zero symbols, which are obtained as intermediate results of the run length encoding. This does not require additional computational costs. It was found that in comparison with the adaptation of the combined encoder structure using direct determination of the arithmetic code volume of each bit plane of DPCM pixel values, the proposed encoder structure provides a significant reduction in computational complexity while maintaining high image compression ratios.

High compression efficiency image compression algorithm based on subsampling for capsule endoscopy

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.

Dynamically tuneable pre‐modulation filter for an airborne PCM/FM telemetry system

In the conventional airborne telemetry system, the pre-modulation filter with a multi-pole active Bessel filter is preferred to use in Pulse Code Modulator (PCM)/FM transmission. In the existing system, it is not possible to change the cut-off frequency for different data rates dynamically as required in the launch scenario of a long-range aerospace vehicle. In general, aerospace vehicle has multiple propellant stages to travel a desired trajectory path. Each stage gets separated from the vehicle at different instances. Each stage measurement plan is defined and correspondingly the PCM format is generated with an optimum data rate. Hence, the telemetry system is required to transmit variable data rates at various instances of a long-range aerospace vehicle from launch point to end point of a vehicle. This can be addressed by designing a dynamically tuneable pre-modulation (DTPM) filter. Here, a suitable DTPM filter scheme is proposed to mitigate variable data rate transmission in the telemetry system. The scheme is analysed as per IRIG-106 standard and simulated using MATLAB. The same has been modelled using VHDL and implemented targeting 28 nm technology Xilinx Zynq FPGA device.

Timing pulse code modulation as a tool for quantization noise reduction in special-purpose IT systems

The aim of the article is to examine the issue of eliminating quantization noise in the process of analogue-to-digital conversion by means of timing pulse code modulation. According to the results of the method proposed, it has established that the use of timing modulation significantly increases the number of sampling levels at a given interval without reducing the duration of individual code pulses, that, in its turn, significantly improves signal quality (readability) after the digital-to-analogue conversion on the receiving side.

Analysis of Speech Fundamental Frequencies for Different Tasks in Japanese

Speech fundamental frequency (SFF) assessment is essential for all dysphonia patients to effectively evaluate the therapeutic effects of voice therapy, especially in patients with disturbances in their voice pitch due to mutational dysphonia, Reinke's edema, or as side effects of hormone therapy. A standard method of SFF measurement remains unknown. Speech tasks such as sustained vowel phonation, counting, reading passage, and spontaneous speech have generally been used for SFF measurements. Ideally, spontaneous speech best reflects SFF; however, this task has not yet been clearly defined and is limited with regard to its adaptation to a clinical setting. A reliable task for SFF measurement in Japanese, which corresponds to a speech task that most closely reflects the value that would be observed with typical spontaneous speech, has not been investigated. This study aimed to identify a reliable speech task by measuring the SFF values elicited by different widely used speech tasks in Japanese, and assess its reliability and coefficient of determination (R2). Sixty healthy volunteers (30 men and 30 women; aged 19-30 years; mean age 22.5 years) were enrolled. All experimental procedures were performed in Japanese. The SFF values for the speech tasks were determined through the voice samples recorded using a Pulse Code Modulation (PCM) recorder. Each task, except spontaneous speech, was repeated five times, and the average fundamental frequency in each task was determined as the SFF. To assess the reliability of the SFF values across daily variations within individual speakers, the SFF measurements were repeated on two different days, separated by at least 1 week. The SFF values of sustained /a/ phonation, sustained vowel-average, counting, reading passage, and spontaneous speech had excellent reliability, in terms of their reproduction based on intraclass correlation. Significantly high SFF values were observed, in decreasing order, for sustained vowels-average, counting, reading passage, and spontaneous speech in both males and females. The highest R2 for spontaneous speech was that of reading passage in both males (R2=0.771) and females (R2=0.806) (P < 0.01). When spontaneous speech was presented as a task most reflective of daily conversation, reading passage was determined to be the reliable task to assess the therapeutic effect of voice therapy in Japanese.

Method for automatic evaluation of the voice signals quality with low bit rate voice coding

Evaluation of the VSQ is an important problem. In known methods for еvaluation the quality of analog or digital voice signals with pulse-code modulation (PCM) are used. When automatic quality assessing, it is assumed that the parameters of voice signal are determined. Then the corresponding subjective assessment of VSQ is selected. Objective methods for quality assessing are obtained by comparing the reference signal and the signal transmitted through the communication channel. To eliminate the identified contradictions, it is proposed to use an objective method that provides the assessing of VSQ in the absence of a reference signal and without conversion to the PCM format, i.e. based on the analysis of the LBR voice digital stream. The analyzed digital stream is considered as a system of random variables, which is characterized by a set of mean values and a covariance matrix that make up the pattern of input implementation. The reference pattern also includes a set of mean values and a covariance matrix. The initial data for the formation of a reference pattern are digital streams formed according to a given protocol of LBR voice coding, having the highest VSQ. The proposed method is based on calculating the divergence between the reference pattern and input implementation pattern. At the training stage, several distorted samples with a known number of bit errors are formed from the reference sample. When comparing their patterns, the divergence values are calculated, which are put in compliance with the VSQ assessment. Two approaches have been identified for making the compliance: experimental and experimental-analytical. The choice of approach is determined by the presence of initial data characterizing the VSQ for particular vocoder. Then generated compliance is interpolated. At the assessment stage for each implementation is calculated the divergence between the explored pattern and the reference pattern, which is formed from the training sample. Based on the divergence, the VSQ assessment is calculated. To test the proposed method a reference sample of digital streams obtained by compressing voice signals with a LPC-10-2400 vocoder was formed. Based on the initial data, which are presented in Recommendation-R F.1112-1, the compliance between the values of sound intelligibility and divergence was compiled by experimental-analytical approach. The obtained correspondence was interpolated by the least squares method using a third degree polynomial.

A 94.2-dB SNDR 142.6-μW VCO-Based Audio ADC With a Split-ADC Differential Pulse Code Modulation Architecture

This letter presents a voltage-controlled oscillator (VCO)-based analog-to-digital converter (ADC) for audio applications using differential pulse code modulation (DPCM) to mitigate VCO voltage-to-frequency nonlinearity. To overcome VCO gain variation, a split-ADC architecture is employed where two sub-ADCs share one calibration unit, thereby enabling a quasideterministic background gain calibration with small delay and fast convergence speed. In addition, a hybrid dynamic element matching (DEM) is employed, which offers a lower delay and a smaller in-band mismatch noise compared to the segmented-tree DEM. Fabricated in 65 nm, the ADC achieves 94.2-dB SNDR, 97.3-dB SNR, and 100.3-dB DR in 20-kHz bandwidth while dissipating 142.6-μW power, yielding a DR-based FoM of 181.8 dB.

Focal-Plane Compression Imager with Increased Quantization Bit Rate and DPCM Error Modeling

Focal-plane processing is the target of many studies due to its potential for enhancing the speed of the vision system flow. With focal-plane processing it is possible to perform parallel processing throughout the entire matrix. In order to alleviate A/D conversion and transmission constraints, analog image compression is implemented at the focal plane, thereby reducing the amount of data to be transmitted and the bandwidth requirements. The ADC is performed at the focal plane as well, after the compression operation whose realization is based on differential pulse-code modulation (DPCM), linear transform and vector quantization (VQ) applied on every 4 × 4 pixel block using current-mode circuits. This paper presents experimental results obtained from a second-generation version of the image sensor. Among these results we can point out the presentation of different captured images and the modeling of errors that were identified during the experimental tests. Since the source of these errors is the DPCM stage, the modeling concerns the bits that refer to the mean block luminance results. The error modeling procedure was developed considering the relationship between the pixel integration period and the DPCM quantizer threshold values. The main contributions of the second-generation chip in comparison to the previous realization are: increase of the vector quantizer complexity, number of bits per pixel, pixel matrix size, and the use of cascode current mirrors in the linear transform matrix. The image sensor advanced in this paper was fabricated in a standard 180 nm CMOS process.

CMOS Image Sensor Featuring Current-Mode Focal-Plane Image Compression

The interest in focal-plane processing techniques, by which image processing is carried out at the pixel level, has increased since the advent of active pixel sensors in the middle 90’s. By sharing processing circuitry by a group of neighboring pixels such techniques enable high-speed imaging operation and massive parallel computation. Focal-plane image compression is particularly interesting, because it allows for further reduction in data rates. The proposed approach also benefits from processing currents rather than voltages, which not only suits current-mode APS imagers, but also enables the circuits to operate at low voltage supply levels and achieve high speed. Moreover, arithmetic computations such as additions and scaling are easily implemented in current mode. Whereas current-mode imaging architectures produce higher fixed pattern noise (FPN) figures than their voltage-mode counterparts, low FPN can be achieved by applying correlated double sampling (CDS) and gain correction techniques. This work presents a 32 × 32 gray-level imaging integrated circuit featuring focal plane image compression, such that for each 4 × 4 pixel block, analog circuits implement differential pulse-code modulation, linear transform, and vector quantization. Other processing functions implemented in the chip are CDS and A/D conversion. Theoretical details are described, as well as the test setup of the chip fabricated in a 0.35 μm CMOS process. To validate the proposed technique, experimental results and captured photographs are shown. The CMOS imager compresses captured images at 0.94 bits/pixel for an overall power consumption below 40 mW (white image), which is equivalent to approximately 36 μW per pixel. Using photographs taken from bar-target pattern inputs, it is shown that details up to 2 cycles/c mare preserved in the decoded images.

A 112-dB SFDR 89-dB SNDR VCO-Based Sensor Front-End Enabled by Background-Calibrated Differential Pulse Code Modulation

This article presents a high-dynamic-range (DR) voltage-controlled oscillator (VCO)-based front end for sensor readout applications. Unlike conventional VCO-based quantizers, which suffer from large voltage-to-frequency non-linearities, the proposed design leverages differential pulse-code modulation (DPCM) from compression theory to substantially reduce the amplitude of the signal incident to the VCO quantizer, thereby achieving an ultra-low total harmonic distortion (THD) of -112 dB. In addition, background digital gain calibration is employed to overcome gain deviation of the VCO, thus ensuring a robust design. Together with dynamic element matching (DEM), the techniques enable a high DR using only the first-order noise shaping inherent in VCO-based quantizers and a moderate 32× oversampling ratio. Fabricated in 65 nm, the sensor front end consumes 3.2- μW power and achieves an SNDR of 89 dB and a DR of 94 dB in 500 Hz of bandwidth. Together with a 1.18- μV <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rms</sub> integrated input-referred noise, it achieves a noise efficiency factor (NEF) of 4 and a Schreier FoM of 171 dB.

Color Image Compression System by using Block Categorization Based on Spatial Details and DCT Followed by Improved Entropy Encoder

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.

Metaheuristic-based vector quantization approach: a new paradigm for neural network-based video compression

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.

Hybrid compression of biomedical ECG and EEG signals based on differential clustering and encoding techniques

AbstractSignal processing techniques incorporated with data compression processes enrich the signals and boost up storage efficiency and transmission reliability. Transmitting uncompressed original data consume wide bandwidth, which increases transmission time and leads to data hammering. These limitations enforce to look for strategic data compression techniques. Lossless compression techniques are requisite where it is important that the original and the decompressed data should be identical or where deviations from the original data would lead to catastrophic consequences, especially in biomedical signal analysis and diagnostics. For which, the input signal preprocessed with differential pulse code modulation (DPCM) reduces the interchannel dependencies to get the desired output. A whole array of unique compression techniques are being utilized in the compression process. The combination of (K‐means clustering, arithmetic encoding [AE], Huffman encoding [HE]) clustering and coding compression techniques are analyzed using electro cardiogram (ECG) and electroencephalogram (EEG) signals. The proposed method employs k‐means clustering combined with Huffman encoding (DiKHE) and k‐means clustering combined with arithmetic encoding (DiKAE) individually. Compression ratio (CR) is analyzed with these combinations of compression techniques for various cluster size K (K = 2,3,4,5,6). A maximum CR of 6.03144 and 4.54126 is obtained for ECG and EEG signals respectively. The compressions based on these techniques are efficient since the compressed signal is reconstructed perfectly as it matches exactly with the original signal.

Distributed mixed signal demultiplexer for electrocorticography electrodes

This work presents a novel architecture, exemplified for electrophysiological applications like ECoG that can be used to detect Epilepsy. The new ECoG is based on a mixed analog-digital architecture (Pulse Amplitude Modulation PAM), that allows the use of thousands of electrodes for recording. Whilst the increased number of electrodes helps to refine the spatial resolution of the medical application, the transmission of the signals from the electrodes to an external analysing device appears to be a bottleneck. To overcoming this, our work presents a hardware architecture and corresponding protocol for a mixed architecture that improves the information density between channels and their signal-to-noise ratio. This is shown by the correlation between the input and the transmitted signals in comparison to a classical digital transmission (Pulse Code Modulation PCM) system. We show in this work that it is possible to transmit the signals of 10 channels with a analog-digital architecture with the same quality of a full digital architecture.

Superpixel based compression of hyperspectral image with modified dictionary and sparse representation

ABSTRACT Sparse representation provides an efficient way for the compression of hyperspectral images in the literature. In this work, an improved version of the Spectral-Spatial Adaptive Sparse Representation (SSASR), Modified SSASR (MSSASR), is proposed for hyperspectral image compression. In the first step of the proposed method, superpixel maps are generated for adaptive spatio-spectral representation. Then, the best possible dictionary is computed for the representation of the data. Afterwards, sparse coefficients are determined depending on the dictionary by Simultaneous Orthogonal Matching Pursuit (SOMP) method. In the final step, the dictionary and sparse coefficients are encoded by quantization and entropy encoding. This paper has the following novelties: modified dictionary learning step, new ordering scheme and Differential Pulse Code Modulation (DPCM) usage. Owing to modified dictionary learning, the sparse coefficients can be represented more compact than traditional SSASR. By using of new ordering scheme, it is not needed to send the superpixel map as side information. Moreover, DCPM usage lowers the magnitudes of sparse coefficients. Thanks to these modifications, the proposed method achieves an important improvement on compression performance. In the experimental results, the proposed method is compared with PCA+JPEG2000, DWT+JPEG2000, 3D-SPECK, 3D-TARP and SSASR methods on Indian Pines, Washington DC Mall, Jasper Ridge and Moffett Field scenes. The evaluation is carried out not only using distance and similarity metrics, namely, signal-to-noise ratio, mean spectral angle and mean spectral correlation metrics but also computation times. Additionally, reconstruction quality in anomaly regions is also used for comparison. Experimental results show that the proposed method outperforms the other compression methods in terms of quality metrics and anomaly preserving performance.