Comparative Analysis of Performance Run Length (RLE) Data Compression Design by VHDL and Design by Microcontroller

Full Text (PDF, 718KB), PP.11-24

Views: 0 Downloads: 0

Author(s)

Marvin Chandra Wijaya 1,*

1. Department of Computer Engineering, Maranatha Christian University, Bandung, Indonesia

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2021.06.02

Received: 26 Jun. 2020 / Revised: 15 Jul. 2020 / Accepted: 26 Aug. 2020 / Published: 8 Dec. 2021

Index Terms

Data Compression, Run Length Encoder, FPGA, VHDL, Microcontroller

Abstract

Compression is a way to compress data to produce a file with a size smaller than its original size. Compression techniques can be performed on text data or binary, image (JPEG, PNG, ....), Audio (MP3, AAC, RMA, WMA, ... ..) and video (MPEG, H261, H263, ....). Compression Data is a way to process information using bits or other information units lower than the representation of data that is not encoded with a particular encoding system.
Data compression has a function to condense, shrink data to its size becomes smaller. With the smaller size of storage space required then less to make it a more efficient storing process, but it also can shorten the time of the data exchange.
Data compression using the run-length encoding (RLE) is a technique used to compress the data contains recurring characters. Run-length encoding (RLE) is a very simple form of data. In RLE running data (sequence data value is the same with many of the data elements in a row) is stored as the value of a single data and calculated the length of the data. This method is useful for data that contains a lot of data, such as simple graphic images (icons, line drawings, and animation). Data compression can be realized in various ways. Data compression can be designed using the VHDL language and can also use a microcontroller. Every realization of data compression has different performances. In this research, the performance was analyzed at the speed of compression. From the experiments conducted, the results of compression speed using VHDL implementation are 6.95 KB / s and microcontroller implementation is 5.34 KB/s. Based on the experimental results from the implementation of data compression using VHDL proposed in this study has a speed of 30.11% better.

Cite This Paper

Marvin Chandra Wijaya, " Comparative Analysis of Performance Run Length (RLE) Data Compression Design by VHDL and Design by Microcontroller", International Journal of Modern Education and Computer Science(IJMECS), Vol.13, No.6, pp. 11-24, 2021.DOI: 10.5815/ijmecs.2021.06.02

Reference

[1] Nelson, Gailly, M.J.L. The Data Compression Book. Second Edition. M&T Books, 1995.

[2] Pu, I. M. Fundamental Data Compression. London: Butterworth Heinemann, 2006.

[3] Patil, R.B, Kulat, K.D., Image and Text Compression Using Dynamic Huffman and RLE Coding, Proceedings of the International Conference on Soft Computing for Problem Solving, pp. 701-708, 2011.

[4] Sujaini, H., Mulyani. Y., Pemampatan File. Institut Teknologi Bandung. Bandung, 2000.

[5] Yuan, H., Guo, K., Sun, X., Ju. Z., A Power-Efficient Test Data Compression Method for SoC using Alternating Statistical Run-Length Coding, Journal of Electronic Testing, Vol 32(1), pp. 59-69, 2016.

[6] Makinen, Ukkonen, Navarro, Approximate Matching of Run-Length Compressed Strings, Algorithmica, Vol 35(4), pp 347-369, 2003

[7] Chen, K.Y., Chao, K.M., A Fully Compressed Algorithm for Computing the Edit Distance of Run-Length Encoded Strings, Algorithmica, vol 65(2), pp 354-370, 2013

[8] Chandra, A., Chakrabaty, K., Analysis of Test Application Time for Test Data Compression Methods Based on Compression Codes, Journal of Electronic Testing, vol 20(2), pp 199-212., 2004.

[9] Tseng, W.D., Lee, L.J., Test Data Compression Using Multi-dimensional Pattern Run-length Codes, Journal of Electronic Testing, vol 26(3), pp 393-400, 2010.

[10] Yuan, H., Mei, J., Song, H., Guo, K., Test Data Compression for System-on-a-Chip using Count Compatible Pattern Run-Length Coding, Journal of Electronic Testing, vol 30(2), pp 237-242, 2014.

[11] Trevisan, L., Vadhan, S., Zuckerman, D., Compression of Samplable Sources, Computationally complexity, vol 14(3), pp 186 – 227, 2005

[12] Jovanovski, J., Arsov, N. Stevanoska, E., Simons, M.S., Velinov, G., A meta-heuristic approach for RLE compression in a column store table, Soft Computing, pp. 1-22, 2018.

[13] Tabra, Y.M., Sabbar, B., FPGA Implementation of New LM-SPIHT Colored Image Compression with Reduced Complexity and Low Memory Requirement Compatible for 5G, International Journal of Reconfigurable and Embedded Systems, pp 1-13, 2019.

[14] Murray, James, D., Van Ryper, William, Encyclopedia of Graphics File Format, 2nd edition, O’Reilly & Associates, Inc, 1996.

[15] Shan, Y., Ren, Y., Zhen., Wang, K., An Enhanced Run-Length Encoding Compression Method for Telemetry Data, Polska Akademia Nauk, pp 551-562, 2017

[16] Mahammad, F.S., Viswanatham, V.M., Performance analysis of data compression algorithms for heterogeneous architecture through parallel approach, The Journal of Supercomputing, pp 1-14, 2018

[17] James, G., Witten. D., Hastie, T., Tibshirani, R., Linear Regression, An Introduction to Statistical Learning, pp 59-126, 2013