Golomb-Rice Coding Research Articles

Hardware optimization for effective switching power reduction during data compression in GOLOMB rice coding.

Loss-less data compression becomes the need of the hour for effective data compression and computation in VLSI test vector generation and testing in addition to hardware AI/ML computations. Golomb code is one of the effective technique for lossless data compression and it becomes valid only when the divisor can be expressed as power of two. This work aims to increase compression ratio by further encoding the unary part of the Golomb Rice (GR) code so as to decrease the amount of bits used, it mainly focuses on optimizing the hardware for encoding side. The algorithm was developed and coded in Verilog and simulated using Modelsim. This code was then synthesised in Cadence Encounter RTL Synthesiser. The modifications carried out show around 6% to 19% reduction in bits used for a linearly distributed data set. Worst-case delays have been reduced by 3% to 8%. Area reduction varies from 22% to 36% for different methods. Simulation for Power consumption shows nearly 7% reduction in switching power. This ideally suggest the usage of Golomb Rice coding technique for test vector compression and data computation for multiple data types, which should ideally have a geometrical distribution.

Signal Acquisition-Independent Lossless Electrocardiogram Compression Using Adaptive Linear Prediction.

In this paper, we propose a lossless electrocardiogram (ECG) compression method using a prediction error-based adaptive linear prediction technique. This method combines the adaptive linear prediction, which minimizes the prediction error in the ECG signal prediction, and the modified Golomb-Rice coding, which encodes the prediction error to the binary code as the compressed data. We used the PTB Diagnostic ECG database, the European ST-T database, and the MIT-BIH Arrhythmia database for the evaluation and achieved the average compression ratios for single-lead ECG signals of 3.16, 3.75, and 3.52, respectively, despite different signal acquisition setup in each database. As the prediction order is very crucial for this particular problem, we also investigate the validity of the popular linear prediction coefficients that are generally used in ECG compression by determining the prediction coefficients from the three databases using the autocorrelation method. The findings are in agreement with the previous works in that the second-order linear prediction is suitable for the ECG compression application.

VLSI Design Based on Block Truncation Coding for Real-Time Color Image Compression for IoT.

It has always been a major issue for a hospital to acquire real-time information about a patient in emergency situations. Because of this, this research presents a novel high-compression-ratio and real-time-process image compression very-large-scale integration (VLSI) design for image sensors in the Internet of Things (IoT). The design consists of a YEF transform, color sampling, block truncation coding (BTC), threshold optimization, sub-sampling, prediction, quantization, and Golomb-Rice coding. By using machine learning, different BTC parameters are trained to achieve the optimal solution given the parameters. Two optimal reconstruction values and bitmaps for each 4 × 4 block are achieved. An image is divided into 4 × 4 blocks by BTC for numerical conversion and removing inter-pixel redundancy. The sub-sampling, prediction, and quantization steps are performed to reduce redundant information. Finally, the value with a high probability will be coded using Golomb-Rice coding. The proposed algorithm has a higher compression ratio than traditional BTC-based image compression algorithms. Moreover, this research also proposes a real-time image compression chip design based on low-complexity and pipelined architecture by using TSMC 0.18 μm CMOS technology. The operating frequency of the chip can achieve 100 MHz. The core area and the number of logic gates are 598,880 μm2 and 56.3 K, respectively. In addition, this design achieves 50 frames per second, which is suitable for real-time CMOS image sensor compression.

Memory-efficient sensor data compression

We treat scalar data compression in sensor network nodes in streaming mode (compressing data points as they arrive, no pre-compression buffering). Several experimental algorithms based on linear predictive coding (LPC) combined with run length encoding (RLE) are considered. In entropy coding stage we evaluated (a) variable-length coding with dynamic prefixes generated with MTF-transform, (b) adaptive width binary coding, and (c) adaptive Golomb-Rice coding. We provide a comparison of known and experimental compression algorithms on 75 sensor data sources. Compression ratios achieved in the tests are about 1.5/4/1000000 (min/med/max), with compression context size about 10 bytes.

VLSI Implementation of Multi-Channel ECG Lossless Compression System

Electrocardiogram (ECG) is used to record the electrical activity of heart. If the instrument monitoring the signal for a long time, it will produce large amount of data. So, the effective lossless ECG compression system can help to reduce the storage space. This brief presents a hardware architecture of multi-channel lossless ECG compression system. The system is based on the algorithm including multi-channel linear prediction and adaptive linear prediction. Also, Golomb rice code (GRC) is used for entropy coding. The hardware implementation has been designed with low hardware complexity usage while using optimum hardware resources. And the architecture can process multiple channels in parallel that can obtain the high throughput. PTB database has been used for verification and testing purposes. This design was implemented in TSMC 180nm. The implementation results show the gate count is 476K and power consumption is 69.18μW while working frequency is 1 KHz.

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.

Distributed video coding based on classification of frequency bands with block texture conditioned key frame encoder for wireless capsule endoscopy

Wireless capsule endoscopy (WCE) has provided remarkable improvement in diagnosing gastrointestinal disorders by scanning the entire digestive tract. The system still need refinement, to upgrade the quality of images, frame rate and battery life. The principal component of the system that can address these issues is low complexity video compressor. Motivated by low computational complexity requirements of WCE video encoding, this paper presents a distributed video coding framework based on frequency bands classification. The lower frequency bands are used to generate good quality side information (SI) as they exhibit high temporal correlation. This reduces the complexity of hash generation at the encoder, thus eliminating the latency in SI creation. Apart from this, SI creation involves only a simple block search and doesn’t depend on Wyner–Ziv (WZ) bands. Also different approach for distributed coding of sub-sampled chroma components of WZ frame is proposed. Low complexity JPEG based key frame encoding is proposed that take advantage of WCE image textural properties to reduce the complexity of encoding smooth blocks by 81% at the quantization and encoding stage. Other novel features include use of discrete Tchebichef transform (DTT), Golomb–Rice code for entropy coding. Performance evaluation shows that the proposed method achieves 60% improvement in compression over Motion JPEG with low computational complexity.

Low cost image compression algorithm with colour reproduction algorithm for transmitting video frame of capsule endoscopy using low power

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.

VLSI Implementation of Lossless ECG Compression Algorithm for Low Power Devices

This brief presents a VLSI implementation of an efficient lossless compression scheme for electrocardiogram (ECG) data encoding to save storage space and reduce transmission time. As compression algorithm is able to save storage space and reduce transmission time, this opportunity has been seized by implementing memory-less design while working at a high clock speed in VLSI. ECG compression algorithm comprises two parts: an adaptive linear prediction technique and content-adaptive Golomb Rice code. An efficient and low power VLSI implementation of compression algorithm has been presented. To improve the performance, the proposed VLSI design uses bit shifting operations as a replacement for the different arithmetic operations. VLSI implementation has been applied to the MIT-BIH arrhythmia database which is able to achieve a lossless bit compression rate of 2.77. Moreover, VLSI architecture contains 3.1 K gate count and core of the chip consumes 27.2 nW of power while working at 1 KHz frequency. The core area is 0.05 mm2 in 90 nm CMOS process.

A near lossless and low complexity image compression algorithm based on fixed threshold DPCM for capsule endoscopy

Compression chip plays a vital role in a wireless capsule endoscopy. It compresses the captured image data to support the limited bandwidth of wireless capsule endoscopy and save the power utilized for transmitting data. This work proposed a near-lossless and low complexity image compression algorithm for wireless capsule endoscopy to provide a high quality image at a good compression ratio with less computational complexity. The algorithm follows four steps, first corner clipping, second RGB-YEN color transform, third fixed threshold DPCM and finally signed Golomb Rice code. The proposed algorithm has a competitive compression ratio of 60.9% compared to other works. It is better than standard JPEG-LS in term of memory usage and computational complexity.

Impact of Misclassification Rates on Compression Efficiency of Red Blood Cell Images of Malaria Infection Using Deep Learning

Malaria is a severe public health problem worldwide, with some developing countries being most affected. Reliable remote diagnosis of malaria infection will benefit from efficient compression of high-resolution microscopic images. This paper addresses a lossless compression of malaria-infected red blood cell images using deep learning. Specifically, we investigate a practical approach where images are first classified before being compressed using stacked autoencoders. We provide probabilistic analysis on the impact of misclassification rates on compression performance in terms of the information-theoretic measure of entropy. We then use malaria infection image datasets to evaluate the relations between misclassification rates and actually obtainable compressed bit rates using Golomb–Rice codes. Simulation results show that the joint pattern classification/compression method provides more efficient compression than several mainstream lossless compression techniques, such as JPEG2000, JPEG-LS, CALIC, and WebP, by exploiting common features extracted by deep learning on large datasets. This study provides new insight into the interplay between classification accuracy and compression bitrates. The proposed compression method can find useful telemedicine applications where efficient storage and rapid transfer of large image datasets is desirable.

Overview of the ways to reduce telemetric information redundancy

The redundancy of telemetric information significantly complicates the real-time processing of the information flows. To speed up the telemetry processing process, the methods for telemetric information redundancy reduction should be applied in order to reduce its flows entering the monitoring systems while maintaining a high speed of processing and reliability of the information. The article provides an overview of the methods for reduction of telemetric information redundancy, such as the increase in PHY-rate of telemetry channel, the Huffman algorithm, the batch mechanism for generation and transmission of telemetry information, the adaptive difference algorithm, the algorithm for transmission of the information based on its representation by residual images, Golomb-Rice codes, reversible compression method. The advantages and disadvantages of each of them are considered. Recommendations on the use of multi-level telemetry information compression system are given, which makes it possible to effectively combine target data compression algorithms that give the highest compression ratio depending on the type of telemetry information transmitted.

An Effective Algorithm and Architecture for the High-Throughput Lossless Compression of High-Resolution Images

This paper proposes a high-throughput lossless image-compression algorithm based on Golomb–Rice coding and its hardware architecture. The proposed solution increases compression ratios (CRs) while preserving the throughput by taking advantage of a novel parallel variable-length sign coding (PVSC) algorithm that reduces the sign bits to achieve a higher CR. In addition, the proposed solution adopts and modifies the two existing compression algorithms to improve the overall compression performance. The experimental results show that the proposed solution yields an average CR of 3.12, which is higher than those achieved with the previous algorithms. The hardware implementation of the proposed solution for an $8 \times 8$ block unit achieves a throughput of 18 GBps and 24 GBps when encoding and decoding, respectively. This hardware performance is enough to handle $7680\times 4320$ @240-Hz image processing.

Optimal Golomb-Rice Code Extension for Lossless Coding of Low-Entropy Exponentially Distributed Sources

This paper presents an extension of Golomb-Rice (GR) code for coding low-entropy sources, which the gap between their entropy and the conventional GR code length gets larger. We mention here the following four facts related to the proposed code, extended-domain GR (XDGR) code: it is represented by multiple code trees, based on the idea of almost instantaneous fixed-to-variable length codes, with its algorithm being a generalization of unary coding; its structure naturally contains run-length coding; the gap between the entropy and its average code length is theoretically guaranteed to be asymptotically negligible as the entropy of the exponentially distributed sources tends to zero; and its coding parameter, corresponding to the negative-domain Rice parameter of GR code, can be estimated from the input source-symbol sequence. Experimental evaluations are also presented supporting the theorems. The proposed XDGR code, having simple algorithm and high compression performance, is expected to be used for many coding applications, which deals with exponentially distributed sources at low bit rates.

An Efficient ECG Lossless Compression System for Embedded Platforms With Telemedicine Applications

This paper presents a method for wireless ECG compression and zero lossless decompression using a combination of three different techniques in order to increase storage space while reducing transmission time. The first technique used in the proposed algorithm is an adaptive linear prediction; it achieves high sensitivity and positive prediction. The second technique is content-adaptive Golomb–Rice coding, used with a window size to encode the residual of prediction error. The third technique is the use of a suitable packing format; this enables the real-time decoding process. The proposed algorithm is evaluated and verified using over 48 recordings from the MIT-BIH arrhythmia database, and it shown to be able to achieve a lossless bit compression rate of $2.83{\times}$ in Lead V1 and $2.77{\times}$ in Lead V2. The proposed algorithm shows better performance results in comparison to previous lossless ECG compression studies in real time; it can be used in data transmission methods for superior biomedical signals for bounded bandwidth across e-health devices. The overall compression system is also built with an ARM M4 processor, which ensures high accuracy performance and consistent results in the timing operation of the system.

Skipped Zonal based Binary DCT (SZBinDCT) for Image communication over Resource Constrained Visual Sensor Network

The contemporary advancements in CMOS technology and Multimedia systems enabled image communication over resource constrained Visual Sensor Network (VSN). However, the size of image data is huge. It is wise to send the compressed image over the bandwidth limited network. Compression algorithms also should be of energy efficient with low complexity and should offer acceptable image quality. In this work an image coder offering low bit rate less than 0.5bpp is aimed. In this paper, a low memory and energy efficient skipped zonal based Binary DCT is proposed that codes the transformed samples using Golomb Rice code. Simulation results offered low bitrate 0.39 bpp with PSNR of 27.34 dB for the standard gray scale test image of Lena size 512 x512 pixels.

Switched Quasi-Logarithmic Quantizer with Golomb–Rice Coding

This paper proposes a model of switched quasi-logarithmic quantizer for speech signal based on G.711 standard with usage of Golomb-Rice (GR) coding. In order to achieve better performances a method with switched quantizer is applied. Variance range is split into quantizers and for each of them a separate quantizer is designed, i.e. the support region is determined. Optimization of the support region and choice of the parameter μ is done in order to obtain a quantizer that obeys G.712 standard and gives minimal average bit rate. Every quantizer within the variance range has own model with a two-stage coder. Two stages are introduced with purpose to reduce the bit rate, whereby GR code plays its role as Variable Length Code (VLC). The first stage uses a GR coder for coding segments of the quantizer’s support region, whereas the second stage applies the coding method with fixed code lengths for coding cells within a segment. GR has simpler and cheaper hardware realization than other VLC codes, Huffman’s for instance, with very satisfying results regarding quality of quantized signal.DOI: http://dx.doi.org/10.5755/j01.eie.23.4.18727

Development of a wireless capsule endoscope system based on field programmable gate array

A new modular and programmable wireless capsule endoscope is presented in this paper. The capsule system consumes low power and has small physical size. A new image compression algorithm is presented in this paper to reduce power consumption and silicon area. The compression algorithm includes color space transform, uniform quantization, sub-sampling, differential pulse code modulation (DPCM) and Golomb-Rice code. The algorithm is tested in a field programmable gate array (FPGA) development board, and the final result achieves 80% compression rate at 40 dB peak signal to noise ratio (PSNR). The algorithm has high image compression efficiency and low power consumption, compared to other existing works. The system is composed of the following three parts: image capsule endoscope, portable wireless receiver and host computer software. The software and hardware design of the three parts are disscussed in details.

Coding Algorithm for Grayscale Images – Design of Piecewise Uniform Quantizer with Golomb–Rice Code and Novel Analytical Model for Performance Analysis

Scalar quantizer selection for processing a signal with a unit variance is a difficult problem, while both selection and quantizer design for the range of variances is even tougher and to the authors’ best knowledge, it is not theoretically solved. Furthermore, performance estimation of various image processing algorithms is unjustifiably neglected and there are only a few analytical models that follow experimental analysis. In this paper, we analyse application of piecewise uniform quantizer with Golomb-Rice coding in modified block truncation coding algorithm for grayscale image compression, propose design improvements and provide a novel analytical model for performance analysis. Besides the nature of input signal, required compression rate and processing delay of the observed system have a strong influence on quantizer design. Consequently, the impact of quantizer range choice is analysed using a discrete designing variance and it was exploited to improve overall quantizer performance, whereas variable-length coding is applied in order to reduce quantizer’s fixed bit-rate. The analytical model for performance analysis is proposed by introducing Inverse Gaussian distribution and it is obtained by discussing a number of images, providing general closed-form solutions for peak-signal-to-noise ratio and the total average bit-rate estimation. The proposed quantizer design ensures better performance in comparison to the other similar methods for grayscale image compression, including linear prediction of pixel intensity and edge-based adaptation, whereas analytical model for performance analysis provides matching with the experimental results within the range of 1 dB for PSQNR and 0.2 bpp for the total average bit-rate.

Predictive Lossless Compression of Regions of Interest in Hyperspectral Images With No-Data Regions

This paper addresses the problem of efficient predictive lossless compression on the regions of interest (ROIs) in the hyperspectral images with no-data regions. We propose a two-stage prediction scheme, where a context-similarity-based weighted average prediction is followed by recursive least square filtering to decorrelate the hyperspectral images for compression. We then propose to apply separate Golomb–Rice codes for coding the prediction residuals of the full-context pixels and boundary pixels, respectively. To study the coding gains of this separate coding scheme, we introduce a mixture geometric model to represent the residuals associated with various combinations of the full-context pixels and boundary pixels. Both information-theoretic analysis and simulations on synthetic data confirm the advantage of the separate coding scheme over the conventional coding method based on a single underlying geometric distribution. We apply the aforementioned prediction and coding methods to four publicly available hyperspectral image data sets, attaining significant improvements over several other state-of-the-art methods, including the shape-adaptive JPEG 2000 method.