The PHY-NGSC-Based ORT Run Length Encoding Scheme for Video Compression

2020 ◽  
Vol 20 (02) ◽  
pp. 2050007
Author(s):  
Poorva Girishwaingankar ◽  
Sangeeta Milind Joshi
Keyword(s):  
Data Compression ◽  
Video Compression ◽  
Code Word ◽  
Run Length ◽  
Compression Speed ◽  
Wide Range ◽  
Secure Computing ◽  
Lossless Data Compression ◽  
Multiple Levels ◽  
Implementation In Matlab

This paper proposes a compression algorithm using octonary repetition tree (ORT) based on run length encoding (RLE). Generally, RLE is one type of lossless data compression method which has duplication problem as a major issue due to the usage of code word or flag. Hence, ORT is offered instead of using a flag or code word to overcome this issue. This method gives better performance by means of compression ratio, i.e. 99.75%. But, the functioning of ORT is not good in terms of compression speed. For that reason, physical- next generation secure computing (PHY-NGSC) is hybridized with ORT to raise the compression speed. It uses an MPI-open MP programming paradigm on ORT to improve the compression speed of encoder. The planned work achieves multiple levels of parallelism within an image such as MPI and open MP for parallelism across a group of pictures level and slice level, respectively. At the same time, wide range of data compression like multimedia, executive files and documents are possible in the proposed method. The performance of the proposed work is compared with other methods like accordian RLE, context adaptive variable length coding (CAVLC) and context-based arithmetic coding (CBAC) through the implementation in Matlab working platform.

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 ΔRLE

2021 ◽  
Vol 45 (1) ◽  
pp. 329-349
Author(s):  
Branslav Mados ◽  
Zuzana Bilanová ◽  
Ján Hurtuk
Keyword(s):  
Data Compression ◽  
Simultaneous Increase ◽  
Original Form ◽  
Memory Consumption ◽  
Compression Efficiency ◽  
Compression Algorithms ◽  
Run Length ◽  
Lossless Data Compression ◽  
Temporal And Spatial ◽  
Basic Features

Lossless data compression algorithms can use statistical redundancy to represent data using a fewer number of bits in comparison to the original uncompressed data. Run-Length Encoding (RLE) is one of the simplest lossless compression algorithms in terms of understanding its principles and software implementation, as well as in terms of temporal and spatial complexity. If this principle is applied to individual bits of original uncompressed data without respecting the byte boundaries, this approach is referred to as bit-level Run-Length Encoding. Lightweight algorithm for lossless data compression proposed in this paper optimizes bit-level RLE data compression, uses special encoding of repeating data blocks, and, if necessary, combines it with delta data transformation or representation of data in its original form intending to increase compression efficiency compared to a conventional bit-level RLE approach. The advantage of the algorithm proposed in this paper is in its low time and memory consumption which are basic features of RLE, along with the simultaneous increase of compression ratio, compared to the classical bit-level RLE approach.

scholarly journals A Productive Expression Platform Derived from Host-Restricted Eilat Virus: Its Extensive Validation and Novel Strategy

Viruses ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 660
Author(s):  
Lu Tan ◽  
Yiwen Zhang ◽  
Xingxing Wang ◽  
Dal Young Kim
Keyword(s):  
Expression Vectors ◽  
Structural Genes ◽  
Host Restriction ◽  
Expression Platform ◽  
Mosquito Cells ◽  
Wide Range ◽  
Restriction Property ◽  
Infected Cells ◽  
Multiple Levels ◽  
Novel Strategy

Most alphaviruses are transmitted by mosquitoes and infect a wide range of insects and vertebrates. However, Eilat virus (EILV) is defective for infecting vertebrate cells at multiple levels of the viral life cycle. This host-restriction property renders EILV an attractive expression platform since it is not infectious for vertebrates and therefore provides a highly advantageous safety profile. Here, we investigated the feasibility of versatile EILV-based expression vectors. By replacing the structural genes of EILV with those of other alphaviruses, we generated seven different chimeras. These chimeras were readily rescued in the original mosquito cells and were able to reach high titers, suggesting that EILV is capable of packaging the structural proteins of different lineages. We also explored the ability of EILV to express authentic antigens via double subgenomic (SG) RNA vectors. Four foreign genetic materials of varied length were introduced into the EILV genome, and the expressed heterologous genetic materials were readily detected in the infected cells. By inserting an additional SG promoter into the chimera genome containing the structural genes of Chikungunya virus (CHIKV), we developed a bivalent vaccine candidate against CHIKV and Zika virus. These data demonstrate the outstanding compatibility of the EILV genome. The produced recombinants can be applied to vaccine and diagnostic tool development, but more investigations are required.

scholarly journals A Model for Analysing the Collective Dynamic Behaviour and Characterising the Exploitation of Population-Based Algorithms

2014 ◽  
Vol 22 (1) ◽  
pp. 159-188 ◽  
Author(s):  
Mikdam Turkey ◽  
Riccardo Poli
Keyword(s):  
Population Dynamics ◽  
Dynamic Behaviour ◽  
Fitness Landscape ◽  
Population Based ◽  
Levels Of Abstraction ◽  
Collective Dynamic ◽  
Model Studies ◽  
Proposed Model ◽  
Wide Range ◽  
Multiple Levels

Several previous studies have focused on modelling and analysing the collective dynamic behaviour of population-based algorithms. However, an empirical approach for identifying and characterising such a behaviour is surprisingly lacking. In this paper, we present a new model to capture this collective behaviour, and to extract and quantify features associated with it. The proposed model studies the topological distribution of an algorithm's activity from both a genotypic and a phenotypic perspective, and represents population dynamics using multiple levels of abstraction. The model can have different instantiations. Here it has been implemented using a modified version of self-organising maps. These are used to represent and track the population motion in the fitness landscape as the algorithm operates on solving a problem. Based on this model, we developed a set of features that characterise the population's collective dynamic behaviour. By analysing them and revealing their dependency on fitness distributions, we were then able to define an indicator of the exploitation behaviour of an algorithm. This is an entropy-based measure that assesses the dependency on fitness distributions of different features of population dynamics. To test the proposed measures, evolutionary algorithms with different crossover operators, selection pressure levels and population handling techniques have been examined, which lead populations to exhibit a wide range of exploitation-exploration behaviours.

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.

Optimal Lossless Data Compression Methodology

2021 ◽  
Author(s):  
Sanjana Rao ◽  
Vidyashree T S ◽  
Manasa M ◽  
Bindushree V ◽  
C. Gururaj
Keyword(s):  
Data Compression ◽  
Lossless Data Compression
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