EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 139 (2022) SHS Web Conf., 139 (2022) 03001 References
Open Access
Issue
SHS Web Conf.
Volume 139, 2022
The 4th ETLTC International Conference on ICT Integration in Technical Education (ETLTC2022)
Article Number 03001
Number of page(s) 6
Section Topics in Computer Science
DOI https://doi.org/10.1051/shsconf/202213903001
Published online 13 May 2022
  1. Perspectives, E. and Report, C., 2021. Cisco Annual Internet Report - Cisco Annual Internet Report (2018–2023) White Paper. Cisco. [Google Scholar]
  2. Rahman, M. and Hamada, M., 2020. Burrows–Wheeler Transform Based Lossless Text Compression Using Keys and Huffman Coding. Symmetry, 12(10), p.1654. [CrossRef] [Google Scholar]
  3. Rahman, M.A. and Hamada, M., 2019, October. A semi-lossless image compression procedure using a lossless mode of JPEG. In 2019 IEEE 13th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) (pp. 143-148). IEEE. [Google Scholar]
  4. Sayood, K., 2017. Introduction to data compression. Morgan Kaufmann. [Google Scholar]
  5. Kotha, H.D., Tummanapally, M. and Upadhyay, V.K., 2019, May. Review on Lossless Compression Techniques. In Journal of Physics: Conference Series (Vol. 1228, No. 1, p. 012007). IOP Publishing. [CrossRef] [Google Scholar]
  6. Rahman, M., Hamada, M. and Shin, J., 2021. The Impact of State-of-the-Art Techniques for Lossless Still Image Compression. Electronics, 10(3), p.360. [CrossRef] [Google Scholar]
  7. Storer, J.A., 1987. Data compression: methods and theory. Computer Science Press, Inc.. [Google Scholar]
  8. Salomon, D. and Motta, G., 2010. Handbook of data compression. London; New York: Springer,. [Google Scholar]
  9. Schiopu, I. and Munteanu, A., 2019. Deep-learning-based lossless image coding. IEEE Transactions on Circuits and Systems for Video Technology, 30(7), pp.1829-1842. [Google Scholar]
  10. Mentzer, F., Agustsson, E., Tschannen, M., Timofte, R. and Gool, L.V., 2019. Practical full resolution learned lossless image compression. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 10629-10638). [Google Scholar]
  11. Mentzer, F., Gool, L.V. and Tschannen, M., 2020. Learning better lossless compression using lossy compression. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 6638-6647). [Google Scholar]
  12. Zhang, Y., 2015, July. The studies and implementation for conversion of image file format. In 2015 10th International Conference on Computer Science & Education (ICCSE) (pp. 190-193). IEEE. [Google Scholar]
  13. Rahman, M.A., Islam, S.M.S., Shin, J. and Islam, M.R., 2018, December. Histogram alternation based digital image compression using Base-2 coding. In 2018 Digital Image Computing: Techniques and Applications (DICTA) (pp. 1-8). IEEE. [Google Scholar]
  14. Rahman, M.A., Rabbi, M.F., Rahman, M.M., Islam, M.M. and Islam, M.R., 2018, September. Histogram modification based lossy image compression scheme using Huffman coding. In 2018 4th International Conference on Electrical Engineering and Information Communication Technology (iCEEiCT) (pp. 279-284). IEEE. [CrossRef] [Google Scholar]
  15. Libpng.org. 2020. PNG specification: Filter Algorithms. [online] Available at: http://www.libpng.org/pub/png/spec/1.2/PNG-Filters.html. [Google Scholar]
  16. Roelofs, G. and Koman, R. 1999. PNG: The definitive guide. O’Reilly & Associates, Inc.. [Google Scholar]
  17. Wilbur, C. 2001. PNG: The definitive guide. Journal of Computing in Higher Education, 12(2), pp.94-97. [CrossRef] [Google Scholar]
  18. Paeth, A.W. 1991. Image file compression made easy. In Graphics Gems II (pp. 93-100). Morgan Kaufmann. [CrossRef] [Google Scholar]
  19. Rahman, M. and Hamada, M., 2019. Lossless image compression techniques: A state-of-the-art survey. Symmetry, 11(10), p.1274. [Google Scholar]
  20. Zhang, J. and Le, T.M. 2010. A new no-reference quality metric for JPEG 2000 images. IEEE Transactions on Consumer Electronics, 56(2), pp.743-750. [CrossRef] [Google Scholar]
  21. Liu, Z., Karam, L.J. and Watson, A.B. 2006. JPEG 2000 encoding with perceptual distortion control. IEEE Transactions on Image Processing, 15(7), pp.1763-1778. [Google Scholar]
  22. Si, Z. and Shen, K., 2016. Research on the WebP image format. In Advanced Graphic Communications, Packaging Technology and Materials (pp. 271-277). Springer, Singapore. [CrossRef] [Google Scholar]
  23. Sneyers, J. and Wuille, P., 2016, September. FLIF: Free lossless image format based on MANIAC compression. In 2016 IEEE International Conference on Image Processing (ICIP) (pp. 66-70). IEEE. [CrossRef] [Google Scholar]
  24. Soferman, N., 2021. FLIF, The New Lossless Image Format That Outperforms PNG, Webp And BPG. [online] Cloudinary. Available at: https://cloudinary.com/blog/flif_the_new_lossless_image_format_that_outperforms_png_webp_and_bpg [Google Scholar]
  25. Flif.info. 2021. FLIF - Free Lossless Image Format. [online] Available at: https://flif.info/. [Google Scholar]
  26. Flif.info. 2021. FLIF - Example. [online] Available at: https://flif.info/example.html [Google Scholar]
  27. Domo.com. 2021. How much data is generated every minute?. [online] Available at: https://www.domo.com/assets/downloads/18_domo_data-never-sleeps-6+verticals.pdf [Accessed 29 September 2021]. [Google Scholar]
  28. Weinberger, M.J., Seroussi, G. and Sapiro, G. 2000. The LOCO-I lossless image compression algorithm: Principles and standardization into JPEG-LS. IEEE Transactions on Image Processing, 9(8), pp.1309-1324. [CrossRef] [Google Scholar]
  29. Weinberger, M.J., Seroussi, G. and Sapiro, G. 1996, March. LOCO-I: A low complexity, context-based, lossless image compression algorithm. In Proceedings of Data Compression Conference-DCC’96 (pp. 140-149). IEEE. [CrossRef] [Google Scholar]
  30. Wu, X., 1996, March. An algorithmic study on lossless image compression. In Proceedings of Data Compression Conference-DCC’96 (pp. 150-159). IEEE. [Google Scholar]
  31. Schiopu, I. and Munteanu, A., 2018, October. Macro-pixel prediction based on convolutional neural networks for lossless compression of light field images. In 2018 25th IEEE International Conference on Image Processing (ICIP) (pp. 445-449). IEEE. [Google Scholar]
  32. Schiopu, I. and Munteanu, A., 2018. Residual-error prediction based on deep learning for lossless image compression. Electronics Letters, 54(17), pp.1032-1034. [CrossRef] [Google Scholar]
  33. Schiopu, I. and Munteanu, A., 2019. Deep-learning-based lossless image coding. IEEE Transactions on Circuits and Systems for Video Technology, 30(7), pp.1829-1842. [Google Scholar]
  34. Van Oord, A., Kalchbrenner, N. and Kavukcuoglu, K., 2016, June. Pixel recurrent neural networks. In International Conference on Machine Learning (pp. 1747-1756). PMLR. [Google Scholar]
  35. Salimans, T., Karpathy, A., Chen, X. and Kingma, D.P., PixelCNN++: A PixelCNN Implementation with Discretized Logistic Mixture. ICLR. [Google Scholar]
  36. Reed, S., Oord, A., Kalchbrenner, N., Colmenarejo, S.G., Wang, Z., Chen, Y., Belov, D. and Freitas, N., 2017, July. Parallel multiscale autoregressive density estimation. In International Conference on Machine Learning (pp. 2912-2921). PMLR. [Google Scholar]
  37. Plenodb.jpeg.org. 2021. JPEG Pleno Database: EPFL Light-field data set. [online] Available at: http://plenodb.jpeg.org/lf/epfl. [Google Scholar]
  38. Ultra Video Group. Tampere University of Technology. Test Sequences. Available: http://ultravideo.cs.tut.fi/#testsequences [Google Scholar]
  39. Chrabaszcz, Patryk and Loshchilov, Ilya and Hutter, Frank. A downsampled variant of imagenet as an alternative to the cifar datasets.arXiv preprint arXiv:1707.08819, 2017. [Google Scholar]
  40. Krasin, I., Duerig, T., Alldrin, N., Ferrari, V., Abu-El-Haija, S., Kuznetsova, A., Rom, H., Uijlings, J., Popov, S., Veit, A. and Belongie, S., 2017. Openimages: A public dataset for large-scale multi-label and multi-class image classification. Dataset available from https://github.com/openimages, 2(3), p.18. [Google Scholar]
  41. Rahman, M.A. and Hamada, M., 2021. Lossless text compression using GPT-2 language model and Huffman coding. In SHS Web of Conferences (Vol. 102, p. 04013). EDP Sciences. [CrossRef] [EDP Sciences] [Google Scholar]
  42. Hoogeboom, E., Peters, J.W., Berg, R.V.D. and Welling, M., 2019. Integer discrete flows and lossless compression. arXiv preprint arXiv:1905.07376. [Google Scholar]
  43. Cao, S., Wu, C.Y. and Krähenbühl, P., 2020. Lossless image compression through super-resolution. arXiv preprint arXiv:2004.02872. [Google Scholar]
  44. Sullivan, G.J., Ohm, J.R., Han, W.J. and Wiegand, T., 2012. Overview of the high efficiency video coding (HEVC) standard. IEEE Transactions on circuits and systems for video technology, 22(12), pp.1649-1668. [CrossRef] [Google Scholar]
  45. Wu, X. and Memon, N., 1997. Context-based, adaptive, lossless image coding. IEEE transactions on Communications, 45(4), pp.437-444. [CrossRef] [Google Scholar]
  46. Rahman, Md Atiqur and Hamada, Mohamed, 2021. PCBMS: A Model to Select an Optimal Lossless Image Compression Technique. IEEE Access, 9, pp.167426-167433. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.