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Comprehensive Review on Lossy and Lossless Compression Techniques

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Abstract

Images are now employed as data in a variety of applications, including medical imaging, remote sensing, pattern recognition, and video processing. Image compression is the process of minimizing the size of images by removing or grouping certain parts of an image file without affecting the quality, thereby saving storage space and bandwidth. Image compression plays a vital role where there is a need for images to be stored, transmitted, or viewed quickly and efficiently. There are different techniques through which images can be compressed. This paper mainly focuses on the survey of basic compression techniques available and the performance metrics that are used to evaluate them. In addition to this, it also provides a review of important pieces of the literature relating to advancements in the fundamental lossy and lossless compression algorithms.

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References

  1. H.M. Wechsler, Digital image processing. Proc. IEEE (2008). https://doi.org/10.1109/proc.1981.12153

    Article  Google Scholar 

  2. S. Y. Irianto, M. Galih, I. Agus, A. Darmawan, and Lindar, Content Based Image Retrieval on Natural and Artificial Images, IOP Conf. Ser.: Mater. Sci. Eng. (2020). https://doi.org/10.1088/1757-899X/917/1/012061.

  3. H. Kumar, S. Gupta, and K. S. Venkatesh, A novel method for image compression using spectrum, (2018), https://doi.org/10.1109/ICAPR.2017.8593179.

  4. A.J. Hussain, A. Al-Fayadh, N. Radi, Image compression techniques: a survey in lossless and lossy algorithms. Neurocomputing (2018). https://doi.org/10.1016/j.neucom.2018.02.094

    Article  Google Scholar 

  5. “Discrete Cosine Transform - MATLAB & Simulink.” https://www.mathworks.com/help/images/discrete-cosine-transform.html (accessed Jul. 05, 2021)

  6. H. Tanaka, K. Ohnishi, Lossy compression of haptic data by using DCT. IEEJ Trans. Ind. Appl. (2010). https://doi.org/10.1541/ieejias.130.945

    Article  Google Scholar 

  7. R. V. P. K. Verma, Use of DWT for Image Compression, Int. J. Sci. Res., (2016)

  8. A. Baviskar, S. Ashtekar, and A. Chintawar, Performance evaluation of high quality image compression techniques (2014) https://doi.org/10.1109/ICACCI.2014.6968643

  9. A.K. Kadhim, A.B.S. Merchany, A. Babakir, An improved image compression technique using EZW and SPHIT algorithms, Ibn AL- Haitham. J. Pure Appl. Sci. (2019). https://doi.org/10.30526/32.2.2121

    Article  Google Scholar 

  10. H. Kanagaraj and V. Muneeswaran, Image compression using HAAR discrete wavelet transform, (2020) https://doi.org/10.1109/ICDCS48716.2020.243596

  11. J. W. Soh, H. S. Lee, and N. I. Cho, An image compression algorithm based on the Karhunen Loève transform, (2018) https://doi.org/10.1109/APSIPA.2017.8282257

  12. X. Wan, Application of K-means Algorithm in Image Compression, (2019) https://doi.org/10.1088/1757-899X/563/5/052042

  13. K.L. Chung, T.C. Hsu, C.C. Huang, Joint chroma subsampling and distortion-minimization-based luma modification for RGB color images with application. IEEE Trans. Image Process. (2017). https://doi.org/10.1109/TIP.2017.2719945

    Article  MathSciNet  Google Scholar 

  14. C. H. Lin, K. L. Chung, and J. P. Fang, Adjusted 4:2:2 chroma subsampling strategy for compressing mosaic videos with arbitrary RGB color filter arrays in HEVC, (2014) https://doi.org/10.1109/APSIPA.2014.7041544.

  15. S. Khaitan and R. Agarwal, Multi-fractal image compression, (2019) https://doi.org/10.1109/COMITCon.2019.8862190

  16. R. Menassel, B. Nini, T. Mekhaznia, An improved fractal image compression using wolf pack algorithm. J. Exp. Theor. Artif. Intell. (2018). https://doi.org/10.1080/0952813X.2017.1409281

    Article  Google Scholar 

  17. K. Rajasekaran, P.D. Sathya, V.P. Sakthivel, Fractal image compression using particle swarm optimization and flower pollination algorithm for medical image. J. Comput. Theor. Nanosci. (2019). https://doi.org/10.1166/jctn.2019.8055

    Article  Google Scholar 

  18. K. Rajasekaran, P. D. Sathya, and V. P. Sakthivel, Application of krill herd algorithm to standard fractal image compression, ARPN J. Eng. Appl. Sci. (2021)

  19. H. Patel, U. Itwala, R. Rana, K. Dangarwala, Survey of lossless data compression algorithms. Int. J. Eng. Res. (2015). https://doi.org/10.17577/ijertv4is040926

    Article  Google Scholar 

  20. S. Anantha Babu, P. Eswaran, C. Senthil Kumar, Lossless compression algorithm using improved RLC for grayscale Image. Arab. J. Sci. Eng. (2016). https://doi.org/10.1007/s13369-016-2082-x

    Article  Google Scholar 

  21. S. A. Babu and E. Perumal, Efficient approach of run length coding technique using lossless grayscale image compression (E-RLC), (2018) https://doi.org/10.1109/ICICT43934.2018.9034377

  22. A. Birajdar, H. Agarwal, M. Bolia, and V. Gupte, Image compression using run length encoding and its optimisation, (2019) https://doi.org/10.1109/GCAT47503.2019.8978464

  23. A. Amin, H. A. Qureshi, M. Junaid, M. Y. Habib, and W. Anjum, Modified run length encoding scheme with introduction of bit stuffing for efficient data compression, (2011)

  24. M. Arif and R. S. Anand, Run length encoding for speech data compression, (2012) https://doi.org/10.1109/ICCIC.2012.6510185.

  25. S. Man, A.P. Utama Siahaan, Huffman text compression technique. Int J Comput Sci Eng (2016). https://doi.org/10.14445/23488387/ijcse-v3i8p124

    Article  Google Scholar 

  26. A. Coding, Chapter 4 arithmetic coding, Spring (2010)

  27. M. A. Kabir and M. R. H. Mondal, Edge-based transformation and entropy coding for lossless image compression, (2017), https://doi.org/10.1109/ECACE.2017.7912997

  28. F. Mentzer, E. Agustsson, M. Tschannen, R. Timofte, and L. Van Gool, Practical full resolution learned lossless image compression, (2019), https://doi.org/10.1109/CVPR.2019.01088

  29. M. Aprilianto and M. Abdurohman, Improvement text compression performance using combination of burrows wheeler transform, move to front, and Huffman coding methods, (2014) https://doi.org/10.1088/1742-6596/495/1/012042.

  30. K. Sharma and K. Gupta, Lossless data compression techniques and their performance, (2017) https://doi.org/10.1109/CCAA.2017.8229810

  31. A. Jain and K. I. Lakhtaria, Comparative study of dictionary based compression algorithms on text data, (2016)

  32. Y. Guo, C. Lu, J. P. Allebach, and C. A. Bouman, Model-based iterative restoration for binary document image compression with dictionary learning, (2017) https://doi.org/10.1109/CVPR.2017.72

  33. M. Ignatoski, J. Lerga, L. Stanković, M. Daković, Comparison of entropy and dictionary based text compression in English, German, French, Italian, Czech, Hungarian, Finnish, and Croatian. Mathematics (2020). https://doi.org/10.3390/MATH8071059

    Article  Google Scholar 

  34. Y. Zu, B. Hua, Parallelizing the deflate compression algorithm on GPU. J. Comput. Inf. Syst. (2015). https://doi.org/10.12733/jcis15020

    Article  Google Scholar 

  35. M. Ledwon, B.F. Cockburn, J. Han, High-throughput FPGA-based hardware accelerators for deflate compression and decompression using high-level synthesis. IEEE Access (2020). https://doi.org/10.1109/ACCESS.2020.2984191

    Article  Google Scholar 

  36. R. Mandale, A. Mhetre, R. Nikam, and B. V.K, Image compression based on prediction coding, Int. J. Megazine Eng. Technol. Manag. Res. (2014)

  37. Urvashi, M. Sood, and E. Puthooran, Resolution adaptive threshold selection for gradient edge predictor in lossless biomedical image compression, Pertanika J. Sci. Technol., (2019)

  38. J. Shukla, M. Alwani, and A. K. Tiwari, A survey on lossless image compression methods, (2010) https://doi.org/10.1109/ICCET.2010.5486344.

  39. P. Annapurna, S. Kothuri, and S. Lukka, Digit recognition using freeman chain code, Int. J. Appl. or Innov. Eng. Manag., (2013)

  40. E. Bribiesca, A new chain code. Pattern Recognit. (1999). https://doi.org/10.1016/S0031-3203(98)00132-0

    Article  Google Scholar 

  41. Y.K. Liu, W. Wei, P. Jie Wang, B. Žalik, Compressed vertex chain codes. Pattern Recognit. (2007). https://doi.org/10.1016/j.patcog.2007.03.001

    Article  MATH  Google Scholar 

  42. R.M. Rodríguez-Dagnino, Compressing bilevel images by means of a three-bit chain code. Opt. Eng. (2005). https://doi.org/10.1117/1.2052793

    Article  Google Scholar 

  43. B. Žalik, D. Mongus, Y.K. Liu, N. Lukač, Unsigned Manhattan chain code. J. Vis. Commun. Image Represent. (2016). https://doi.org/10.1016/j.jvcir.2016.03.001

    Article  Google Scholar 

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Elakkiya, S., Thivya, K.S. Comprehensive Review on Lossy and Lossless Compression Techniques. J. Inst. Eng. India Ser. B 103, 1003–1012 (2022). https://doi.org/10.1007/s40031-021-00686-3

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  • DOI: https://doi.org/10.1007/s40031-021-00686-3

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