Bootstrap Analysis of Compression Algorithms
Keywords:Compression Algorithm, Statistical Analysis, Bootstrap
Compression algorithms have been proposed with the technology advance. However, there are not objective analysis procedures to guide a future choice of an algorithm directed for the type of data in the system they are intended for. This paper introduces a statistical framework, based on the bootstrap method, to execute the analysis of compression algorithms using an objective comparison parameter as a criterion. A case study using the compression ratio as the parameter and file samples of 4 different types was analyzed. The proposed scheme allowed us to infer which algorithm is better to be used for each data type. RLE has proven more suitable to image, audio and video files with Huffman obtaining comparable performance. For text files, LZW has remarkably outperformed all other algorithms.
S. Frankel and S. Krishnan, “Ip security (ipsec) and internet key exchange (ike) document roadmap,” Internet Requests for Comments, RFC Editor, RFC 6071, February 2011.
A. Freier, P. Karlton, and P. Kocher, “The secure sockets layer (ssl) protocol version 3.0,” Internet Requests for Comments, RFC Editor, RFC 6101, August 2011.
A. B. Jambek and N. A. Khairi, “Performance comparison of huffman and lempel-ziv welch data compression for wireless sensor node application,” American Journal of Applied Sciences, vol. 11, no. 1, pp. 119–126, 2014. [Online]. Available: https://thescipub.com/abstract/ajassp.2014.119.126
A. Gupta, A. Bansal, and V. Khanduja, “Modern lossless compression techniques: Review, comparison and analysis,” in 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE, 2017, pp. 1–8.
T. Hidayat, M. H. Zakaria, and N. C. Pee, “Comparison of lossless compression schemes for WAV audio data 16-bit between huffman and coding arithmetic,” International journal of simulation: systems, science & technology, Feb. 2019. [Online]. Available: https://doi.org/10.5013/ijssst.a.19.06.36
M. A. Rahman and M. Hamada, “Lossless image compression techniques: A state-of-the-art survey,” Symmetry, vol. 11, no. 10, p. 1274, Oct. 2019. [Online]. Available: https://doi.org/10.3390/sym11101274
B. Efron and R. Tibshirani, An Introduction to the Bootstrap, ser. Chapman & Hall/CRC Monographs on Statistics & Applied Probability. Taylor & Francis, 1994.
A. Hussain, A. Al-Fayadh, and N. Radi, “Image compression techniques: A survey in lossless and lossy algorithms,” Neurocomputing, vol. 300, pp. 44–69, 2018.
K. Kalajdzic, S. H. Ali, and A. Patel, “Rapid lossless compression of short text messages,” Computer Standards & Interfaces, vol. 37, pp. 53–59, 2015.
G. Campobello, O. Giordano, A. Segreto, and S. Serrano, “Comparison of local lossless compression algorithms for wireless sensor networks,” Journal of Network and Computer Applications, vol. 47, pp. 23–31, 2015.
A. Antony and S. Ganapathy, “Highly efficient near lossless video compression using selective intra prediction for hevc lossless mode,” AEUE - International Journal of Electronics and Communications, vol. 69, no. 11, pp. 1650–1658, 2015.
F. Ghido and I. Tabus, “Sparse modeling for lossless audio compression,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 21, no. 1, pp. 14–28, 2013.
A. Masmoudi and W. Puech, “Lossless chaos-based crypto-compression scheme for image protection,” IET Image Processing, vol. 8, no. 12, pp. 671–686, 2014.
C.-C. Chang, T.-C. Lu, G. Horng, and Y.-H. Huang, “Very efficient variable-length codes for the lossless compression of vq indices,” Multimedia Tools and Applications, vol. 75, no. 6, pp. 3537–3552, 2016.
M. T. Asif, K. Srinivasan, N. Mitrovic, J. Dauwels, and P. Jaillet, “Nearlossless compression for large traffic networks,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 4, pp. 1817–1826, 2015.
L. C. Souza, R. M. Souza, G. J. Amaral, and T. M. S. Filho, “A parametrized approach for linear regression of interval data,” Knowledge-Based Systems, vol. 131, pp. 149 – 159, 2017.
L. C. de Souza, R. M. C. R. de Souza, and G. J. A. do Amaral, “Dynamic clustering of interval data based on hybrid Lq distance,” Knowledge and Information Systems, May 2019.
C. McAnlis and A. Haecky, Understanding Compression: Data Compression for Modern Developers. O’Reilly Media, 2016.
Z. Li and M. Drew, Fundamentals of Multimedia. Pearson Prentice Hall, 2004.
D. Salomon, G. Motta, and D. Bryant, Data Compression: The Complete Reference. Springer London, 2007.
A. C. Davison and D. V. Hinkley, Bootstrap Methods and their Application, ser. Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press, 1997.
W. L. Martinez and A. R. Martinez, Computational statistics handbook with MATLAB. Chapman and Hall/CRC, 2015.