scholarly journals A Review on Different Types of Lossless Data Compression Techniques

2021 ◽  
pp. 50-56
Author(s):  
Anshul Gupta ◽  
Sumit Nigam
Keyword(s):  
Data Compression ◽  
Lossless Compression ◽  
Data Representation ◽  
Communication Cost ◽  
File Size ◽  
Network Bandwidth ◽  
Different Types ◽  
Lossless Data Compression

This paper provides different kinds of techniques for lossless data compression and comparison between them. By eliminating redundant bits, data compression decreases the file size. In order to reduce the capacity needed for that data, it decreases the redundant bits in data representation and thus uses the bandwidth effectively to reduce the communication cost. Compression of data saves file volume, network bandwidth and speeds up the transfer speed as well. Lossless and Lossy are the two techniques for data compression. Lossless compression maintains the data properly.

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 Constructing Binary Huffman Tree

2013 ◽  
Vol 21 (2) ◽  
pp. 133-143
Author(s):  
Hiroyuki Okazaki ◽  
Yuichi Futa ◽  
Yasunari Shidama
Keyword(s):  
Data Compression ◽  
Binary Code ◽  
Lossless Compression ◽  
Huffman Coding ◽  
Compression Algorithms ◽  
Huffman Encoding ◽  
Lossless Data Compression ◽  
Entropy Encoding

Summary Huffman coding is one of a most famous entropy encoding methods for lossless data compression [16]. JPEG and ZIP formats employ variants of Huffman encoding as lossless compression algorithms. Huffman coding is a bijective map from source letters into leaves of the Huffman tree constructed by the algorithm. In this article we formalize an algorithm constructing a binary code tree, Huffman tree.

scholarly journals Performance optimization of dual stage algorithm for lossless data compression and decompression

2018 ◽  
Vol 7 (2.21) ◽  
pp. 127
Author(s):  
Shrikanth Shirakol ◽  
Akshata Koparde ◽  
Sandhya . ◽  
Shravan Kulkarni ◽  
Yogesh Kini
Keyword(s):  
Data Compression ◽  
Performance Optimization ◽  
Lossless Compression ◽  
Arithmetic Coding ◽  
Real Numbers ◽  
Dual Stage ◽  
Text Information ◽  
Lossless Data Compression ◽  
Pass Through ◽  
Lzw Algorithm

In this paper, an optimized dual stage architecture is proposed which is the combination of Lempel-Ziv-Welch (LZW) Algorithm at the first phase and Arithmetic Coding being the later part of Architecture. LZW Algorithm is a lossless compression algorithm and code here for each character is available in the dictionary which reduces 5-bits per cycle as compared to ASCII. In arithmetic coding the numbers are represented by an interval of real numbers from zero to one according to their probabilities. It is an entropy coding and is lossless in nature. The text information is allowed to pass through the proposed architecture and it gets compressed to the higher rate.  

scholarly journals Framework For Lossless Data Compression Using Python

2019 ◽  
Vol 8 (03) ◽  
pp. 24575-24585
Author(s):  
Manas Malik
Keyword(s):  
Data Compression ◽  
Video Compression ◽  
Lossless Compression ◽  
Frame Rate ◽  
Command Line ◽  
Python Language ◽  
Lossless Data Compression ◽  
Implementation Aspects ◽  
Audio Video ◽  
Ongoing Project

A lot has been done in the field of data compression, yet we don’t have a proper application for compressing daily usage files. There are appropriate and very specific tools online that provide files to be compressed and saved, but the content we use for streaming our videos, be it a Netflix video or a gaming theater play, data consumed is beyond the calculation of a user. Back-end developers know all about it and as developers we have acknowledged it but not yet achieved it in providing on an ease level. Since the user would not never be concerned about compression, developers can always take initiative while building the application to provide accessibility with compression before-hand. We have decided to create a framework that will provide all the functionality needed for a developer to add this feature. Making use of the python language this process can work. I’m a big fan of Python, mostly because it has a vibrant developer community that has helped produce an unparalleled collection of libraries that enable one to add features to applications quickly. For the DEFLATE lossless compression, has a higher level of abstraction provided by the zlib C library, in Python it is generally provided by the Python zlib library which is an interface, we have a lot to do including the audio, video and subtitles of the file. We also make use of the fabulous ffmpy library.  ffmpy is a Python library that provides access to the ffmpeg command line utility. ffmpeg is a command-line application that can perform several different kinds of transformations on video files, including video compression, which is the most commonly requested feature of ffmpeg. Frame rate and audio synchronization are few other parameters to look closely. This is an ongoing project and there remains few implementation aspects, data compression remains a concern when touched upon the design. We along with python community intend to solve this issue.

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

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4602
Author(s):  
Shinichi Yamagiwa ◽  
Yuma Ichinomiya
Keyword(s):  
Data Compression ◽  
High Speed ◽  
Lossless Compression ◽  
Differential Pulse ◽  
Video Stream ◽  
Video Encoding ◽  
Pulse Code Modulation ◽  
Differential Pulse Code Modulation ◽  
Code Modulation ◽  
Lossless Data Compression

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.

scholarly journals Efficient Lossless Compression of Multitemporal Hyperspectral Image Data

2018 ◽  
Vol 4 (12) ◽  
pp. 142 ◽  
Author(s):  
Hongda Shen ◽  
Zhuocheng Jiang ◽  
W. Pan
Keyword(s):  
Data Compression ◽  
Hyperspectral Image ◽  
Image Data ◽  
Lossless Compression ◽  
Large Data ◽  
Hyperspectral Data ◽  
Compression Performance ◽  
Temporal Correlations ◽  
Sensing Applications ◽  
Data Volume

Hyperspectral imaging (HSI) technology has been used for various remote sensing applications due to its excellent capability of monitoring regions-of-interest over a period of time. However, the large data volume of four-dimensional multitemporal hyperspectral imagery demands massive data compression techniques. While conventional 3D hyperspectral data compression methods exploit only spatial and spectral correlations, we propose a simple yet effective predictive lossless compression algorithm that can achieve significant gains on compression efficiency, by also taking into account temporal correlations inherent in the multitemporal data. We present an information theoretic analysis to estimate potential compression performance gain with varying configurations of context vectors. Extensive simulation results demonstrate the effectiveness of the proposed algorithm. We also provide in-depth discussions on how to construct the context vectors in the prediction model for both multitemporal HSI and conventional 3D HSI data.

Joint Source-Channel Coding in Dictionary Methods of Lossless Data Compression

2010 ◽  
Vol 56 (4) ◽  
pp. 351-355
Author(s):  
Marcin Rodziewicz
Keyword(s):  
Data Compression ◽  
Channel Coding ◽  
Data Stream ◽  
Noisy Channels ◽  
Error Resiliency ◽  
Joint Source Channel Coding ◽  
Lossless Data Compression ◽  
Compressed Data ◽  
Source Channel Coding

Joint Source-Channel Coding in Dictionary Methods of Lossless Data Compression Limitations on memory and resources of communications systems require powerful data compression methods. Decompression of compressed data stream is very sensitive to errors which arise during transmission over noisy channels, therefore error correction coding is also required. One of the solutions to this problem is the application of joint source and channel coding. This paper contains a description of methods of joint source-channel coding based on the popular data compression algorithms LZ'77 and LZSS. These methods are capable of introducing some error resiliency into compressed stream of data without degradation of the compression ratio. We analyze joint source and channel coding algorithms based on these compression methods and present their novel extensions. We also present some simulation results showing usefulness and achievable quality of the analyzed algorithms.

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