Lossless Audio Coding based on Burrows Wheeler Transform and Run Length Encoding Algorithm

International Journal of Electronics and Communication Engineering

© 2015 by SSRG - IJECE Journal

Volume 2 Issue 10

Year of Publication : 2015

Authors : Pratibha Warkade and Agya Mishra

10.14445/23488549/IJECE-V2I10P106

How to Cite?

Pratibha Warkade and Agya Mishra, "Lossless Audio Coding based on Burrows Wheeler Transform and Run Length Encoding Algorithm," SSRG International Journal of Electronics and Communication Engineering, vol. 2,  no. 10, pp. 24-33, 2015. Crossref, https://doi.org/10.14445/23488549/IJECE-V2I10P106

Abstract:

 In this paper we present a new lossless audio coding algorithm using Burrows-Wheeler Transform (BWT) and Run Length Encoding (RLE).Audio signals used are assumed to be of floating point values. The BWT is applied to the audio signals to get the transformed coefficients and then these resulting coefficients are better compressed using Run Length Encoding. Two entropy coding are used which are Run Length Encoding and Huffman coding. Proposed compression algorithm is experimented and analyzed for two different stereo type audio signals. Compression ratio and Bit rate for audio coding has been used as a comparison parameter for proposed audio coding algorithm. Experimental result shows that the lossless audio coding algorithm outperforms other lossless audio coding methods; using combined Burrows Wheeler Transform & Move to front coding method ,using combined Burrows Wheeler Transform and Huffman coding method, and using Burrows Wheeler Transform ,Move to front coding method & Run Length Encoding method.

Keywords:

 Audio Coding, Burrows-Wheeler Transform (BWT), Bit rate, Compression ratio, Huffman Coding(HC), Move to front coding(MTF) and Run Length Encoding(RLE).

References:

[1] Hend. A. Elsayed,“Burrows-Wheeler Transform and Combination of Move-to-Front Coding and Run Length Encoding for Lossless Audio Coding”, 9th International Conference on Computer Engineering & Systems (ICCES), IEEE, pp.354 -359, Dec. 2014 .
[2] Chapter 6: A Word Level Context Based Speech Coding Scheme.
[3] H. A. Elsayed and M. Alghoniemy, “Lossless Audio Coding Using Burrows Wheeler Transform and Move-to-Front Coding,” International Conference on Computer Engineering & Systems (ICCES), IEEE,pp.209-212 ,Nov. 2007.
[4] M.K.Borahi and V.K.Govindan, "A Simple Lossless Audio Data Compression Based On Burrows Wheeler Transform," International Conference on Electrical Engineering and Computer Science(ICEECS),pp. 68-73 ,April. 2013.
[5] I Made Agus Dwi Suarjaya ,“A New Algorithm for Data Compression Optimization” , International Journal of Advanced Computer Science and Applications (IJACSA), Vol. 3, No.8, pp.14- 17,Sep.2012.
[6] M.P.Bhuyan, V.Deka, S.Bordoloi, “Burrows Wheeler Based Data Compression And Secure Transmission”,International Journal of Research in Engineering and Technology(IJRET), Vol.2 , No.2 , pp.14-19,Dec.2013 .
[7] Rafeeq Mohammad and M. Vijaya Kumar, “Audio Compression using Multiple Transformation Techniques”International Journal of Computer Applications (IJCA) ,Vol. 86 , No. 13, pp.9-14,Jan. 2014 .
[8] Paul Brittan, “Evaluating Lossless Data Compression Algorithms for Use on Mobile Devices”, Literature Synthesis ,2008.
[9] Dengfeng Zhang, Qing Liu+,Yuewan Wu, Yaping Li, Lin Xiao, “Compression and Indexing Based on BWT: A Survey”, 10th Web Information System and Application Conference,IEEE,pp.61- 64,Nov.2013.
[10] Asral Bahari Jambek and Nor Alina Khairi “Performance Comparison Of Huffman And Lempel-Ziv Welch Data Compression For Wireless Sensor Node Application”, American Journal of Applied Sciences(AJAS), pp.119-126, Accepted on Dec.2014.
[11] Hari Narayan Mishra, Agya mishra, “Audio Enhancement Using Remez Exchange Algorithm with DWT”,SSRG International Journal of Electronics and Communication Engineering (SSRGIJECE), Vol.2,No.2,pp.60-65,Feb 2015.