Performance Analysis of Recent Algorithms for Compression of Various Medical Images
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-54547-4_3
Reference15 articles.
1. Chen, Z., Gu, S., Lu, G., Xu, D.: Exploiting Intra-Slice and Inter-Slice Redundancy for Learning-Based Lossless Volumetric Image Compress. In: Budhewar, A.S., Doye, D.D. (eds.) Performance Analysis of Compression Techniques for Multimedia Data. In: Studies in Fuzziness and Soft Computing, pp. 243–256. IEEE (2019). https://doi.org/10.1007/978-3-030-03131-2_12.ion. IEEE Trans. Image Process. 31, 1697–1707 (2022). https://doi.org/10.1109/TIP.2022.3140608
2. Krishnaswamy, R., NirmalaDevi, S.: Efficient medical image compression based on integer wavelet transform. In: 2020 Sixth International Conference on Bio Signals, Images, and Instrumentation (ICBSII), pp. 1–5. IEEE (2020). https://doi.org/10.1109/ICBSII49132.2020.9167597
3. Budhewar, A.S., Doye, D.D.: Performance analysis of compression techniques for multimedia data. In: Studies in Fuzziness and Soft Computing, pp. 243–256. IEEE (2019). https://doi.org/10.1007/978-3-030-03131-2_12
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5. Kumar, B.P.S., Ramanaiah, K.V.: An integrated medical image compression approach with deep learning and binary plane difference. In: 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT), pp. 202–205. IEEE (2019). https://doi.org/10.1109/ICSSIT46314.2019.8987944
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