2(1), (2023):1-7 DOI: https://doi.org/10.46632/jdaai/2/1/1
P. Nithya, T. Vengattaraman, M. Sathya
The rapid development in the hardware and the software gives rise to data growth. This data growth has numerous impacts, including the need for a larger storage capacity for storing and transmitting. Data compression is needed in today’s world because it helps to minimize the amount of storage space required to store and transmit data. Performance measures in data compression are used to evaluate the efficiency and effectiveness of data compression algorithms. In recent times, numerous data compression algorithms are developed to reduce data storage and increase transmission speed in this internet era. In order to analyses how data compression performance is measured in terms of text, image, audio, and video compressions. This survey presents discussion made for important data compression parameters according to their data types.
?Jayasankar, V. Thirumal, and D. Ponnurangam, “A survey on data compression techniques: From the perspective of data quality, coding schemes, data type and applications,” Journal of King Saud University – Computer and Information Sciences, vol. 33, no. 2, pp. 119–140, Feb. 2021, doi: 10.1016/j.jksuci.2018.05.006.
Hosseini, “A Survey of Data Compression Algorithms and their Applications,” 2012, doi: 10.13140/2.1.4360.9924.
Boopathiraja, V. Punitha, P. Kalavathi, and V. B. S. Prasath, “Computational 2D and 3D Medical Image Data Compression Models,” Arch Computat Methods Eng, vol. 29, no. 2, pp. 975–1007, Mar. 2022, doi: 10.1007/s11831-021-09602-w.
Tanvi Patel, Judith Angela, Poonam Choudhary, Kruti Dangarwala, and Sad Vidhya Mandal Institue of Technology, “Survey of Text Compression Algorithms,” IJERT, vol. V4, no. 03, p. IJERTV4IS030932, Mar. 2015, doi: 10.17577/IJERTV4IS030932.
A. Rahman and M. Hamada, “Burrows–Wheeler Transform Based Lossless Text Compression Using Keys and Huffman Coding,” Symmetry, vol. 12, no. 10, p. 1654, Oct. 2020, doi: 10.3390/sym12101654.
Habib, M. J. Islam, and M. S. Rahman, “A dictionary-based text compression technique using quaternary code,” Iran J Comput Sci, vol. 3, no. 3, pp. 127–136, Sep. 2020, doi: 10.1007/s42044-019-00047-w.
Oswald and B. Sivaselvan, “An optimal text compression algorithm based on frequent pattern mining,” J Ambient Intell Human Comput, vol. 9, no. 3, pp. 803–822, Jun. 2018, doi: 10.1007/s12652-017-0540-2.
Rehman, M. Sharif, and M. Raza, “Image Compression: A Survey,” RJASET, vol. 7, no. 4, pp. 656–672, Jan. 2014, doi: 10.19026/rjaset.7.303.
H. Rasheed, O. M. Salih, M. M. Siddeq, and M. A. Rodrigues, “Image compression based on 2D Discrete Fourier Transform and matrix minimization algorithm,” Array, vol. 6, p. 100024, Jul. 2020, doi: 10.1016/j.array.2020.100024.
Kr. Mondal and A. Debnath, “Developing a Dynamic Cluster Quantization based Lossless Audio Compression (DCQLAC),” Multimed Tools Appl, vol. 80, no. 6, pp. 8257–8280, Mar. 2021, doi: 10.1007/s11042-020-09886-3.
Zhang and D. R. Bull, “A Parametric Framework for Video Compression Using Region-Based Texture Models,” IEEE J. Sel. Top. Signal Process., vol. 5, no. 7, pp. 1378–1392, Nov. 2011, doi: 10.1109/JSTSP.2011.2165201.
W. Soh et al., “Reduction of Video Compression Artifacts Based on Deep Temporal Networks,” IEEE Access, vol. 6, pp. 63094–63106, 2018, doi: 10.1109/ACCESS.2018.2876864.
Koval, V. Yatskiv, I. Yakymenko, and D. Zahorodnia, “A Lossless Image Compression Algorithm Based On Group Encoding,” in 2020 10th International Conference on Advanced Computer Information Technologies (ACIT), Deggendorf, Germany, Sep. 2020, pp. 871–874. doi: 10.1109/ACIT49673.2020.9208909.
T. Selvi, J. Amudha, and R. Sudhakar, “Medical image encryption and compression by adaptive sigma filterized synorr certificateless signcryptive Levenshtein entropy-coding-based deep neural learning,” Multimedia Systems, vol. 27, no. 6, pp. 1059–1074, Dec. 2021, doi: 10.1007/s00530-021-00764-y.
M. Yasin and A. M. Abdulazeez, “Image Compression Based on Deep Learning: A Review,” AJRCoS, pp. 62–76, May 2021, doi: 10.9734/ajrcos/2021/v8i130193.
Xu, J. Mou, J. Liu, and J. Hao, “The image compression–encryption algorithm based on the compression sensing and fractional-order chaotic system,” Vis Comput, vol. 38, no. 5, pp. 1509–1526, May 2022, doi: 10.1007/s00371-021-02085-7.
Golts and Y. Y. Schechner, “Image compression optimized for 3D reconstruction by utilizing deep neural networks,” Journal of Visual Communication and Image Representation, vol. 79, p. 103208, Aug. 2021, doi: 10.1016/j.jvcir.2021.103208.
Datta, B. Jana, and M. D. Chakraborty, “Two-layers robust data hiding scheme for highly compressed image exploiting AMBTC with difference expansion,” Journal of King Saud University – Computer and Information Sciences, vol. 34, no. 8, pp. 5240–5260, Sep. 2022, doi: 10.1016/j.jksuci.2022.05.013.
M. Salih, M. H. Rasheed, M. M. Siddeq, and M. A. Rodrigues, “Image compression for quality 3D reconstruction,” Journal of King Saud University – Computer and Information Sciences, vol. 34, no. 5, pp. 2271–2287, May 2022, doi: 10.1016/j.jksuci.2020.07.012.
Mishra, S. K. Singh, and R. K. Singh, “Wavelet-Based Deep Auto Encoder-Decoder (WDAED)-Based Image Compression,” IEEE Trans. Circuits Syst. Video Technol., vol. 31, no. 4, pp. 1452–1462, Apr. 2021, doi: 10.1109/TCSVT.2020.3010627.
Dimililer, “DCT-based medical image compression using machine learning,” SIViP, vol. 16, no. 1, pp. 55–62, Feb. 2022, doi: 10.1007/s11760-021-01951-0.
Patidar, S. Kumar, and D. Kumar, “A Review on Medical Image Data Compression Techniques,” in 2nd International Conference on Data, Engineering and Applications (IDEA), Bhopal, India, Feb. 2020, pp. 1–6. doi: 10.1109/IDEA49133.2020.9170679.
Krishnaswamy and S. NirmalaDevi, “EFFICIENT MEDICAL IMAGE COMPRESSION BASED ON INTEGER WAVELET TRANSFORM,” in 2020 Sixth International Conference on Bio Signals, Images, and Instrumentation (ICBSII), Chennai, India, Feb. 2020, pp. 1–5. doi: 10.1109/ICBSII49132.2020.9167597.
A. Hilal and H. A. Hilal, “Arabic Text Lossless Compression by Characters Encoding,” Procedia Computer Science, vol. 155, pp. 618–623, 2019, doi: 10.1016/j.procs.2019.08.087.
A. Hilal and H. A. Hilal, “Turkish Text Compression via Characters Encoding,” Procedia Computer Science, vol. 175, pp. 286–291, 2020, doi: 10.1016/j.procs.2020.07.042.
Zhang et al., “TADOC: Text analytics directly on compression,” The VLDB Journal, vol. 30, no. 2, pp. 163–188, Mar. 2021, doi: 10.1007/s00778-020-00636-3.
Vijayalakshmi, “Lossless Text Compression Technique Based on Static Dictionary for Unicode Tamil Document”.
Abu Hilal, H. Abu Hilal, and A. Abu Hilal, “Multistage Arabic and Turkish Text Compression via Characters Encoding and 7-Zip,” JUSPN, vol. 15, no. 01, pp. 11–15, Mar. 2021, doi: 10.5383/JUSPN.15.01.002.
Jacob, P. Somvanshi, and R. Tornekar, “Comparative Analysis of Lossless Text Compression Techniques,” IJCA, vol. 56, no. 3, pp. 17–21, Oct. 2012, doi: 10.5120/8871-2850.
D. Research Scholar, Department of Computer Science, Vidyasagar College of Arts and Science, Udumalpet, Tamilnadu, India., B. Vijayalakshmi*, Dr. N. Sasirekha, and Associate Professor, Department of Computer Science, Vidyasagar College of Arts and Science, Udumalpet, Tamilnadu, India., “Lossless Tamil Compression using ASCII Substitution and Modified Huffman Encoding Technique,” IJRTE, vol. 8, no. 6, pp. 2900–2906, Mar. 2020, doi: 10.35940/ijrte.F8177.038620.
Vijayalakshmi and D. N. Sasirekha, “Comparative Analysis of Lossless Text Compression Methods with Novel Tamil Compression Technique”.
Kim, D. Jun, B.-G. Kim, S. Beack, M. Lee, and T. Lee, “Two-Dimensional Audio Compression Method Using Video Coding Schemes,” Electronics, vol. 10, no. 9, p. 1094, May 2021, doi: 10.3390/electronics10091094.
Shukla, M. Ahirwar, R. Gupta, S. Jain, and D. S. Rajput, “Audio Compression Algorithm using Discrete Cosine Transform (DCT) and Lempel-Ziv-Welch (LZW) Encoding Method,” in 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), Faridabad, India, Feb. 2019, pp. 476–480. doi: 10.1109/COMITCon.2019.8862228.
Mineo and H. Shouno, “Improving sign-algorithm convergence rate using natural gradient for lossless audio compression,” J AUDIO SPEECH MUSIC PROC., vol. 2022, no. 1, p. 12, Dec. 2022, doi: 10.1186/s13636-022-00243-w.
C. da Silva Stanisce Corrêa, R. Pirk, and M. da Silva Pinho, “Lossy Audio Compression Applied to Launch Vehicle Acoustic Data,” J Astronaut Sci, vol. 68, no. 2, pp. 535–548, Jun. 2021, doi: 10.1007/s40295-021-00257-0.
Tu, Y. Yang, B. Du, W. Yang, X. Zhang, and J. Zheng, “RNN-based signal classification for hybrid audio data compression,” Computing, vol. 102, no. 3, pp. 813–827, Mar. 2020, doi: 10.1007/s00607-019-00713-8.
Banerjee, S. Chatterjee, and S. Das Bit, “An energy saving audio compression scheme for wireless multimedia sensor networks using spatio-temporal partial discrete wavelet transform,” Computers & Electrical Engineering, vol. 48, pp. 389–404, Nov. 2015, doi: 10.1016/j.compeleceng.2015.09.009.
Sablatash and T. Cooklev, “Compression of High-Quality Audio Signals, Including Recent Methods Using Wavelet Packets,” Digital Signal Processing, vol. 6, no. 2, pp. 96–107, Apr. 1996, doi: 10.1006/dspr.1996.0010.
Chen, Q. Liu, and Y. Yang, “Adaptive Multi-Modality Residual Network for Compression Distorted Multi-View Depth Video Enhancement,” IEEE Access, vol. 8, pp. 97072–97081, 2020, doi: 10.1109/ACCESS.2020.2996258.
Park and M. Kim, “Deep Predictive Video Compression Using Mode-Selective Uni- and Bi-Directional Predictions Based on Multi-Frame Hypothesis,” IEEE Access, vol. 9, pp. 72–85, 2021, doi: 10.1109/ACCESS.2020.3046040.
Lu, X. Zhang, W. Ouyang, L. Chen, Z. Gao, and D. Xu, “An End-to-End Learning Framework for Video Compression,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 43, no. 10, pp. 3292–3308, Oct. 2021, doi: 10.1109/TPAMI.2020.2988453.
Wang, Q. Peng, E. Wang, K. Han, and W. Xiang, “Region-of-Interest Compression and View Synthesis for Light Field Video Streaming,” IEEE Access, vol. 7, pp. 41183–41192, 2019, doi: 10.1109/ACCESS.2019.2907572.
Kim and H.-J. Lee, “A low-power surveillance video coding system with early background subtraction and adaptive frame memory compression,” IEEE Trans. Consumer Electron., vol. 63, no. 4, pp. 359–367, Nov. 2017, doi: 10.1109/TCE.2017.015073.
P. Nithya, T. Vengattaraman, M. Sathya. “Survey On Parameters of Data Compression.” REST Journal on Data Analytics and Artificial Intelligence 2(1), (2023):1-7.