Lossless Compression Technique for Real-Time Photoplethysmographic Measurements

2015 ◽  
Vol 64 (4) ◽  
pp. 975-983 ◽  
Author(s):  
Rajarshi Gupta
Keyword(s):  
Real Time ◽  
Lossless Compression ◽  
Compression Technique

scholarly journals Real-Time Lossless Compression for Ultra-High-Density Synchrophasor and Point on Wave Data

2021 ◽  
pp. 1-1
Author(s):  
Chang Chen ◽  
Weikang Wang ◽  
Yin He ◽  
Lingwei Zhan ◽  
Yilu Liu
Keyword(s):  
Real Time ◽  
Lossless Compression ◽  
High Density ◽  
Wave Data

A Computationally Efficient Switching Based Lossless Compression Algorithm for Natural Images

2012 ◽  
Vol 433-440 ◽  
pp. 6540-6545
Author(s):  
Vineet Khanna ◽  
Hari Singh Choudhary ◽  
Prakrati Trivedi
Keyword(s):  
Computational Complexity ◽  
Compression Ratio ◽  
Lossless Compression ◽  
Adaptive Threshold ◽  
Least Square ◽  
Natural Images ◽  
Prediction Algorithm ◽  
Computationally Efficient ◽  
Compression Technique ◽  
Hybrid Combination

This paper presents a new image lossless compression technique for natural images. The proposed algorithm uses the switching of existing Edge Directed Prediction algorithm and gradient Adaptive Predictor (GAP ) methods. The proposed algorithm is a switching based algorithm and the criteria of switching are based upon the adaptive threshold. We know that EDP has higher computational complexity due to the estimation of LS (Least Square ) based paramter whereas GAP has relatively lower computational complexity. So, in order to reduce the computational complexity we had made a hybrid combination of EDP and GAP Method which the proposed algorithm is a generic algorithm and it produces the best results in different varieties of images in terms of both compression ratio and computational complexity.

Real-time lossless compression of microarray images by separate compaction of foreground and background

2015 ◽  
Vol 39 ◽  
pp. 34-43 ◽  
Author(s):  
N. Karimi ◽  
S. Samavi ◽  
S. Shirani ◽  
A. Banaei ◽  
E. Nasr-Esfahani
Keyword(s):  
Real Time ◽  
Lossless Compression

Online Data Compression Technique for Real Time Data of Energy Management System in the Industrial Production

2014 ◽  
Vol 519-520 ◽  
pp. 70-73 ◽  
Author(s):  
Jing Bai ◽  
Tie Cheng Pu
Keyword(s):  
Data Compression ◽  
Real Time ◽  
Energy Management ◽  
Industrial Production ◽  
Energy Management System ◽  
Sampled Data ◽  
Time Data ◽  
Online Data ◽  
Compression Technique ◽  
Real Time Data

Aiming at storing and transmitting the real time data of energy management system in the industrial production, an online data compression technique is proposed. Firstly, the auto regression model of a group of sequence is established. Secondly, the next sampled data can be predicted by the model. If the estimated error is in the allowable range, we save the parameters of model and the beginning data. Otherwise, we save the data and repeat the method from the next sampled data. At Last, the method is applied in electricity energy data compression of a beer production. The application result verifies the effectiveness of the proposed method.

Design of Real-Time Lossless Compression System Based on DSP and FPGA

2012 ◽  
Vol 433-440 ◽  
pp. 4173-4177
Author(s):  
Jian Hu Zhan ◽  
Wen Yi Liu
Keyword(s):  
Data Compression ◽  
Real Time ◽  
Removal Rate ◽  
Lossless Compression ◽  
Telemetry System ◽  
Compression Device ◽  
Hardware System ◽  
Data Capacity ◽  
Noise Data ◽  
Lossless Data Compression

The application of the lossless data compression technology in the filed of telemetry system is discussed in this paper. Based on the ARC algorithm, a real-time lossless data compression technology is proposed. By combining the TMS320C6416 and XC3S200AN FPGA, this paper designs a real-time lossless data compression device hardware system. 2048 bytes of some telemetry noise data can be compressed in 5.64ms in this system and the compression removal rate reaches 78%. What’s more, the system has solved the problem of data capacity and speed during the process of data compression , which greatly improves the efficiency of data compression.

scholarly journals A Fast and Efficient Lossless Compression Technique for Greyscale Images

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Compression Ratio ◽  
Service Providers ◽  
Computing Time ◽  
Computation Time ◽  
Lossless Compression ◽  
Medical Data ◽  
Compression Technique ◽  
Scanning Method ◽  
Remote Healthcare ◽  
Encoding Method

The growth of cloud based remote healthcare and diagnosis services has resulted, Medical Service Providers (MSP) to share diagnositic data across diverse environement. This medical data are accessed across diverse platforms, such as, mobile and web services which needs huge memory for storage. Compression technique helps to address and solve storage requirements and provides for sharing medical data over transmission medium. Loss of data is not acceptable for medical image processing. As a result, this work considers lossless compression for medical in particular and in general any greyscale images. Modified Huffman encoding (MH) is one of the widely used technique for achieving lossless compression. However, due to longer bit length of codewords the existing Modified Huffman (MH) encoding technique is not efficient for medical imaging processing. Firstly, this work presents Modified Refined Huffman (MRH) for performing compression of greyscale and binary images by using diagonal scanning method. Secondly, to minimize the computing time parallel encoding method is used. Experiments are conducted for wide variety of images and performance is evaluated in terms of Compression Ratio, Computation Time and Memory Utilization. The proposed MRH achieves significant performance improvement in terms of Compression Ratio, Computation Time and Memory Usage over its state-of-the-art techniques, such as, LZW, CCITT G4, JBIG2 and Levenberg–Marquardt (LM) Neural Network algorithm. The overall results achieved show the applicability of MRH for different application services.

scholarly journals Real-Time Lossless Compression Algorithm for Ultrasound Data Using BL Universal Code

2018 ◽  
Author(s):  
Jung Hoon Kim ◽  
Sunmi Yeo ◽  
Jong Won Kim ◽  
Kyeongsoon Kim ◽  
Tai-kyong Song ◽  
...  
Keyword(s):  
Real Time ◽  
Ultrasound Imaging ◽  
Data Transfer ◽  
Lossless Compression ◽  
Imaging Systems ◽  
Universal Code ◽  
High Data ◽  
High Flexibility

Software-based ultrasound imaging systems provide high flexibility that allows easy and fast adoption of newly developed algorithms. However, the extremely high data rate required for data transfer from sensors (e.g., transducers) to the ultrasound imaging systems is a major bottleneck in the software-based architecture, especially in the context of real-time imaging. To overcome this limitation, in this paper, we present a Binary cLuster (BL) code, which yields an improved compression ratio compared to the exponential Golomb code. Owing to the real-time encoding/decoding features without overheads, the universal code is a good solution to reduce the data transfer rate for software-based ultrasound imaging. The performance of the proposed method was evaluated using in vitro and in vivo data sets. It was demonstrated that the BL-beta code has a good stable lossless compression performance of 20 ~ 30% while requiring no auxiliary memory or storage.

scholarly journals Integrated HNAS Network Model Based Lossless Compression with Data Hiding using Parity Check in Medical Images

2020 ◽  
Vol 9 (4) ◽  
pp. 2656-2662
Keyword(s):  
Network Model ◽  
Data Hiding ◽  
Compression Ratio ◽  
Reversible Data Hiding ◽  
Medical Image ◽  
Medical Images ◽  
Care Delivery ◽  
Lossless Compression ◽  
Parity Check ◽  
Compression Technique

A massive volume of medical data is generating through advanced medical image modalities. With advancements in telecommunications, Telemedicine, and Teleradiologyy have become the most common and viable methods for effective health care delivery around the globe. For sufficient storage, medical images should be compressed using lossless compression techniques. In this paper, we aim at developing a lossless compression technique to achieve a better compression ratio with reversible data hiding. The proposed work segments foreground and background area in medical images using semantic segmentation with the Hierarchical Neural Architecture Search (HNAS) Network model. After segmenting the medical image, confidential patient data is hidden in the foreground area using the parity check method. Following data hiding, lossless compression of foreground and background is done using Huffman and Lempel-Ziv-Welch methods. The performance of our proposed method has been compared with those obtained from standard lossless compression algorithms and existing reversible data hiding methods. This proposed method achieves better compression ratio and a hundred percent reversible when data extraction.

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