Differential Pulse Code Modulation Research Articles

Cipher-image compression by using compressive sensing and interweaving permutation

Recently, compressing cipher images by using compressive sensing (CS) has attracted a lot of attention. However, the previous CS-based works cannot provide satisfactory compression performance. In this article, a cipher-image compression scheme based on CS and interweaving permutation is proposed to address this concern. Before image compression, the nature image is down-sampled to generate a lot of patches by using interweaving permutation firstly and then each patch is encrypted by a scrambling matrix, where each patch is a down-sampled image. In this way, the encrypted patches are highly correlated from one to each other since they preserve the correlation of the original patches. Therefore, we can encode the cipher image by employing CS and differential pulse-code modulation (DPCM) in an efficient way. To reconstruct the image effectively, an iterative shrinkage-thresholding (IST) algorithm is developed. Compared with the previous CS-based works, our scheme achieves better ratio-distortion (R-D) performance.

Gated recurrent unit predictor model-based adaptive differential pulse code modulation speech decoder

Speech coding is a method to reduce the amount of data needs to represent speech signals by exploiting the statistical properties of the speech signal. Recently, in the speech coding process, a neural network prediction model has gained attention as the reconstruction process of a nonlinear and nonstationary speech signal. This study proposes a novel approach to improve speech coding performance by using a gated recurrent unit (GRU)-based adaptive differential pulse code modulation (ADPCM) system. This GRU predictor model is trained using a data set of speech samples from the DARPA TIMIT Acoustic-Phonetic Continuous Speech Corpus actual sample and the ADPCM fixed-predictor output speech sample. Our contribution lies in the development of an algorithm for training the GRU predictive model that can improve its performance in speech coding prediction and a new offline trained predictive model for speech decoder. The results indicate that the proposed system significantly improves the accuracy of speech prediction, demonstrating its potential for speech prediction applications. Overall, this work presents a unique application of the GRU predictive model with ADPCM decoding in speech signal compression, providing a promising approach for future research in this field.

A State Table SPHIT Approach for Modified Curvelet-based Medical Image Compression

Medical imaging plays a significant role in clinical practice. Storing and transferring a large volume of images can be complex and inefficient. This paper presents the development of a new compression technique that combines the fast discrete curvelet transform (FDCvT) with state table set partitioning in the hierarchical trees (STS) encoding scheme. The curvelet transform is an extension of the wavelet transform algorithm that represents data based on scale and position. Initially, the medical image was decomposed using the FDCvT algorithm. The FDCvT algorithm creates symmetrical values for the detail coefficients, and these coefficients are modified to improve the efficiency of the algorithm. The curvelet coefficients are then encoded using the STS and differential pulse-code modulation (DPCM). The greatest amount of energy is contained in the coarse coefficients, which are encoded using the DPCM method. The finest and modified detail coefficients are encoded using the STS method. A variety of medical modalities, including computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI), are used to verify the performance of the proposed technique. Various quality metrics, including peak signal-to-noise ratio (PSNR), compression ratio (CR), and structural similarity index (SSIM), are used to evaluate the compression results. Additionally, the computation time for the encoding (ET) and decoding (DT) processes is measured. The experimental results showed that the PET image obtained higher values of the PSNR and CR. The CT image provides high quality for the reconstructed image, with an SSIM value of 0.96 and the fastest ET of 0.13 seconds. The MRI image has the shortest DT, which is 0.23 seconds.

Digital radio-over-fiber system based on differential pulse code modulation and space division multiplexing.

In this paper, we present and experimentally demonstrate a digital-radio-over-fiber (D-RoF) architecture based on differential pulse code modulation (DPCM) and space division multiplexing (SDM). At low quantization resolution, DPCM can effectively reduce quantization noise and obtain significant signal-to-quantization noise ratio (SQNR) gain. We experimentally study the 7-core and 8-core multicore fiber transmission of 64-ary quadrature amplitude modulation (64QAM) orthogonal frequency division multiplexing (OFDM) signals with a bandwidth of 100 MHz in a fiber-wireless hybrid transmission link. Compared to PCM-based D-RoF, the error vector magnitude (EVM) performance in the DPCM-based D-RoF is effectively improved when the quantization bits (QBs) are 3-5 bits. In particular, when the QB is 3 bits, the EVM of the DPCM-based D-RoF is 6.5% and 7% lower than that of the PCM-based system in 7-core- and 8-core-multicore fiber-wireless hybrid transmission links, respectively.

Neuro-fuzzy image compression using differential pulse code modulation and probabilistic decision making

Neuro-fuzzy image compression using differential pulse code modulation and probabilistic decision making

Pixel Based Techniques for Gray Image Compression: A review

Currently, with the huge increase in modern communication and network applications, the speed of transformation and storing data in compact forms are pressing issues. Daily an enormous amount of images are stored and shared among people every moment, especially in the social media realm, but unfortunately, even with these marvelous applications, the limited size of sent data is still the main restriction's, where essentially all these applications utilized the well-known Joint Photographic Experts Group (JPEG) standard techniques, in the same way, the need for construction of universally accepted standard compression systems urgently required to play a key role in the immense revolution.
 This review is concerned with Differential pulse code modulation (DPCM) and pixel-based techniques, where the spatial domain is exploited to compress images efficiently in terms of compression performance and preserving quality. The new pixel-based method overcomes predictive coding constraints with fewer residues and higher compression ratios.

Differentially private publication of database streams via hybrid video coding

While most anonymization technology available today is designed for static and small data, the current picture is of massive volumes of dynamic data arriving at unprecedented velocities. From the standpoint of anonymization, the most challenging type of dynamic data is data streams. However, while the majority of proposals deal with publishing either count-based or aggregated statistics about the underlying stream, little attention has been paid to the problem of continuously publishing the stream itself with differential privacy guarantees. In this work, we propose an anonymization method that can publish multiple numerical-attribute, finite microdata streams with high protection as well as high utility, the latter aspect measured as data distortion, delay and record reordering. Our method, which relies on the well-known differential pulse-code modulation scheme, adapts techniques originally intended for hybrid video encoding, to favor and leverage dependencies among the blocks of the original stream and thereby reduce data distortion. The proposed solution is assessed experimentally on two of the largest data sets in the scientific community working in data anonymization. Our extensive empirical evaluation shows the trade-off among privacy protection, data distortion, delay and record reordering, and demonstrates the suitability of adapting video-compression techniques to anonymize database streams.

Analysis of the Measurement Matrix in Directional Predictive Coding for Compressive Sensing of Medical Images

Compressive sensing of 2D signals involves three fundamental steps: sparse representation, linear measurement matrix, and recovery of the signal. This paper focuses on analyzing the efficiency of various measurement matrices for compressive sensing of medical images based on theoretical predictive coding. During encoding, the prediction is efficiently chosen by four directional predictive modes for block-based compressive sensing measurements. In this work, Gaussian, Bernoulli, Laplace, Logistic, and Cauchy random matrices are used as the measurement matrices. While decoding, the same optimal prediction is de-quantized. Peak-signal-to-noise ratio and sparsity are used for evaluating the performance of measurement matrices. The experimental result shows that the spatially directional predictive coding (SDPC) with Laplace measurement matrices performs better compared to scalar quantization (SQ) and differential pulse code modulation (DPCM) methods. The results indicate that the Laplace measurement matrix is the most suitable in compressive sensing of medical images.

A 178.9-dB FoM 128-dB SFDR VCO-Based AFE for ExG Readouts With a Calibration-Free Differential Pulse Code Modulation Technique

This article presents a voltage-controlled oscillator (VCO)-based analog front end (AFE) for ExG readout applications with both a wide dynamic range (DR) and high linearity. By using a differential pulse code modulation (DPCM) technique, VCO non-linearity is mitigated by operating the VCO in the small-signal linear regime. To minimize power consumption from the power-hungry gain error calibration, the effect of VCO gain variation within the DPCM loop is investigated, leading to a calibration-free DPCM via a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math></inline-formula> truncator. At the circuit level, a coarse–fine phase decoding scheme is proposed to increase signal-to-quantization-noise ratio (SQNR) while avoiding metastability issues. Fabricated in 65 nm, the AFE occupies an area of 0.056 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . It achieves 94.2-dB SNDR and 95.1-dB DR in 500 Hz of bandwidth while dissipating 1.68- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> power from a single 0.8-V supply, yielding a Schreier FoM of 178.9 dB. Importantly, the techniques enable a virtually distortion-less spectrum with a noise-floor limited spurious-free dynamic range (SFDR) of 128 dB. In addition, the AFE is validated with ECG and EOG recording, demonstrating complete signal capturing in the presence of motion/stimulation artifacts.

Lossless Compression Algorithm and Architecture for Reduced Memory Bandwidth Requirement with Improved Prediction Based on the Multiple DPCM Golomb-Rice Algorithm

In a computing environment, higher resolutions generally require more memory bandwidth, which inevitably leads to the consumption more power. This may become critical for the overall performance of mobile devices and graphic processor units with increased amounts of memory access and memory bandwidth. This paper proposes a lossless compression algorithm with a multiple differential pulse-code modulation variable sign code Golomb-Rice to reduce the memory bandwidth requirement. The efficiency of the proposed multiple differential pulse-code modulation is enhanced by selecting the optimal differential pulse code modulation mode. The experimental results show compression ratio of 1.99 for high-efficiency video coding image sequences and that the proposed lossless compression hardware can reduce the bus bandwidth requirement.

Overview of the Screen Content Support in VVC: Applications, Coding Tools, and Performance

In an increasingly connected world, consumer video experiences have diversified away from traditional broadcast video into new applications with increased use of non-camera-captured content such as computer screen desktop recordings or animations created by computer rendering, collectively referred to as screen content. There has also been increased use of graphics and character content that is rendered and mixed or overlaid together with camera-generated content. The emerging Versatile Video Coding (VVC) standard, in its first version, addresses this market change by the specification of low-level coding tools suitable for screen content. This is in contrast to its predecessor, the High Efficiency Video Coding (HEVC) standard, where highly efficient screen content support is only available in extension profiles of its version 4. This paper describes the screen content support and the five main low-level screen content coding tools in VVC: transform skip residual coding (TSRC), block-based differential pulse-code modulation (BDPCM), intra block copy (IBC), adaptive color transform (ACT), and the palette mode. The specification of these coding tools in the first version of VVC enables the VVC reference software implementation (VTM) to achieve average bit-rate savings of about 41% to 61% relative to the HEVC test model (HM) reference software implementation using the Main 10 profile for 4:2:0 screen content test sequences. Compared to the HM using the Screen-Extended Main 10 profile and the same 4:2:0 test sequences, the VTM provides about 19% to 25% bit-rate savings. The same comparison with 4:4:4 test sequences revealed bit-rate savings of about 13% to 27% for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Y'C_{B}C_{R}$ </tex-math></inline-formula> and of about 6% to 14% for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R'G'B'$ </tex-math></inline-formula> screen content. Relative to the HM without the HEVC version 4 screen content coding extensions, the bit-rate savings for 4:4:4 test sequences are about 33% to 64% for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Y'C_{B}C_{R}$ </tex-math></inline-formula> and 43% to 66% for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R'G'B'$ </tex-math></inline-formula> screen content.

Sequentially Adaptive Differential Pulse Code Modulation Using Adaptive LSP Filters.(Dept.E)

Sequentially Adaptive Differential Pulse Code Modulation Using Adaptive LSP Filters.(Dept.E)

A Comparative Analysis of Image Compression using PCM and DPCM

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

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.

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.

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.