A comparative analysis on various block truncation methods in the E-learning environment

Document Type : Research Paper


1 Research and Development Centre, Bharathiar University, Coimbatore, India

2 Department of CSE, Mepco Schlenk Engineering College, Sivakasi, India


Image compression and Image processing are the two aspects that affect image specific e-learning environment. In this regard, there are various methods proposed to process and compress the image effectively. Recent works mainly concentrate on finding the memory complexity and processing complexity of various techniques. According to that, block truncation models are widely applied over various e-learning fields. Block Truncation Model (BTM) considers the images as a collection of individual blocks to be processed. These blocks are extracted and evaluated for image compression. To compress the images, the least important blocks need to be ignored or suppressed. At this stage, standard BTC, Absolute Moment BTC (AMBTC), Machine Learning (ML) based BTC and Deep Learning (DL) based BTC techniques have emerged from various resources. This work is analyzing various BTC models in terms of time efficiency, memory efficiency and computation efficiency. The results shown in this work reveal the detailed comparisons of e-learning based block truncation models.


[1] X. Chai, H. Wu, Z. Gan, Y. Zhang, Y. Chen and K.W. Nixon, An efficient visually meaningful image compression
and encryption scheme based on compressive sensing and dynamic LSB embedding, Optics Lasers Engin. 124
(2020) 105837.
[2] Y.W. Dai, X.H. Chen, S.Y. Cho and H.Q. Zhou, Critical exponents of block-block mutual information in onedimensional infinite lattice systems, Phys. Rev. E 104(4) (2021) 044137.
[3] N. Krishnaraj, M. Elhoseny, M. Thenmozhi, M.M. Selim and K. Shankar, Deep learning model for real-time image
compression in Internet of Underwater Things (IoUT), J. Real-Time Image Process. 17(6) (2020) 2097–2111.
[4] W.L. Lyu and C.C. Chang, An image compression method based on block truncation coding and linear regression,
J. Inf. Hiding Multim. Signal Process. 7(1) (2016) 198–215.
[5] K. Nagarathinam and R.K. Soundar, Moving shadow detection based on stationary wavelet transform, EURASIP
J. Image Video Process. 2017(1) (2017) 1–11.
[6] K. Nagarathinam and R.K. Soundar, Moving shadow detection based on stationary wavelet transform and Zernike
moments, IET J. Comput. Vision 12(6) (2018).[7] S.I. Olsen, Block truncation and planar image coding, Pattern Recognit. Letters, 21(13–14) (2000) 1141–1148.
[8] N.V. Soniminde and S.D. Thepade, Performance improvement of content-based video retrieval using Thepade’s
sorted n-ary block truncation coding (TSnBTC) with various color spaces, Int. Conf. Commun. Inf. Comput.
Technol. IEEE (2021) 1–5.
[9] R. K. Soundar and M. Karuppasamy, Identification of untrained facial image in combined global and local preserving feature space, Int. J. Biomet. Bioinf. 4(1) (2010).
[10] J. Uthayakumar, M. Elhoseny and K. Shankar, Highly reliable and low-complexity image compression scheme
using neighborhood correlation sequence algorithm in WSN, IEEE Trans. Reliab. 69(4) (2020) 1398–1423.
Volume 12, Special Issue
December 2021
Pages 2087-2092
  • Receive Date: 02 October 2021
  • Revise Date: 18 November 2021
  • Accept Date: 05 December 2021