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Research Article

An image compression model via adaptive vector quantization: hybrid optimization algorithm

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Pages 259-277 | Received 25 Jul 2019, Accepted 23 Oct 2022, Published online: 07 Dec 2022

References

  • Lee SW, Kim HY. An energy-efficient low-memory image compression system for multimedia IoT products. J Image Video Proc. 2018;87; doi:10.1186/s13640-018-0333-3.
  • Ernawan F, Kabir MN, Zain JM. Bit allocation strategy based on Psychovisual threshold in image compression. Multimed Tools Appl. 2018;77:13923–13946. doi:10.1007/s11042-017-4999-9.
  • Sabbavarapu SR, Gottapu SR, Bhima PR. A discrete wavelet transform and recurrent neural network based medical image compression for MRI and CT images. J Ambient Intell Human Comput. 2021;12:6333–6345. doi:10.1007/s12652-020-02212-7.
  • Kumar H, Gupta S, Venkatesh KS. A novel non-customary method of image compression based on image spectrum. Sādhanā. 2020;45:288, doi:10.1007/s12046-020-01519-7.
  • Kumar SS, Mangalam H. Quantization based wavelet transformation technique for digital image compression with removal of multiple artifacts and noises. Cluster Comput. 2019;22:11271–11284. doi:10.1007/s10586-017-1379-1.
  • Kasban H, Hashima S. Adaptive radiographic image compression technique using hierarchical vector quantization and Huffman encoding. J Ambient Intell Human Comput. 2019;10:2855–2867. doi:10.1007/s12652-018-1016-8.
  • S. S. Parikh, D. Ruiz, H. Kalva, G. Fernández-Escribano and V. Adzic, “High bit-depth medical image compression with HEVC,” in IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 2, pp. 552-560, March 2018. doi:10.1109/JBHI.2017.2660482.
  • D. Tellez, G. Litjens, J. van der Laak and F. Ciompi, “Neural image compression for gigapixel histopathology image analysis,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 2, pp. 567-578, 1 Feb. 2021. doi:10.1109/TPAMI.2019.2936841.
  • C. Cai, L. Chen, X. Zhang and Z. Gao, “Efficient variable rate image compression with multi-scale decomposition network,” in IEEE Transactions on Circuits and Systems for Video Technology, vol. 29, no. 12, pp. 3687-3700, Dec. 2019. doi:10.1109/TCSVT.2018.2880492.
  • D. Rossinelli, G. Fourestey, F. Schmidt, B. Busse and V. Kurtcuoglu, “High-throughput lossy-to-lossless 3d image compression,” in IEEE Transactions on Medical Imaging, vol. 40, no. 2, pp. 607-620, Feb. 2021. doi:10.1109/TMI.2020.3033456.
  • S. Liu, W. Bai, N. Zeng and S. Wang, “A fast fractal based compression for MRI images,” in IEEE Access, vol. 7, pp. 62412–62420, 2019. doi:10.1109/ACCESS.2019.2916934.
  • J. Wang, Y. Duan, X. Tao, M. Xu and J. Lu, “Semantic perceptual image compression with a Laplacian pyramid of convolutional networks,” in IEEE Transactions on Image Processing, vol. 30, pp. 4225–4237, 2021. doi:10.1109/TIP.2021.3065244.
  • L. -H. Chen, C. G. Bampis, Z. Li, A. Norkin and A. C. Bovik, “ProxIQA: a proxy approach to perceptual optimization of learned image compression,” in IEEE Transactions on Image Processing, vol. 30, pp. 360–373, 2021. doi:10.1109/TIP.2020.3036752.
  • S. Zha, T. N. Pappas and D. L. Neuhoff, “Hierarchical lossy bilevel image compression based on cutset sampling,” in IEEE Transactions on Image Processing, vol. 30, pp. 1527–1541, 2021. doi:10.1109/TIP.2020.3043587.
  • A. Zheng, G. Cheung and D. Florencio, “Joint denoising/compression of image contours via shape prior and context tree,” in IEEE Transactions on Image Processing, vol. 27, no. 7, pp. 3332–3344, July 2018. doi:10.1109/TIP.2018.2816818.
  • B. Zhang, P. V. Sander, C. Tsui and A. Bermak, “Microshift: an efficient image compression algorithm for hardware,” in IEEE Transactions on Circuits and Systems for Video Technology, vol. 29, no. 11, pp. 3430–3443, Nov. 2019. doi:10.1109/TCSVT.2018.2880227.
  • Golts A, Schechner YY. “Image compression optimized for 3D reconstruction by utilizing deep neural networks,” Journal of Visual Communication and Image Representation 2 July 2021Volume 79 (Cover date: August 2021), 103208.
  • Chen C, Li Y-L, Huang L. “An entropy minimization histogram mergence scheme and its application in image compression,” Signal Processing: Image Communication 4 September 2021Volume 99 (Cover date: November 2021), 116422.
  • Wang Z, Ding G, Li F. “Deep image compression with multi-stage representation,” Journal of Visual Communication and Image Representation 21 July 2021Volume 79 (Cover date: August 2021), 103226.
  • Xu S, Zhang J, Yuan D. “Singular vector sparse reconstruction for image compression,” Computers & Electrical Engineering9 March 2021Volume 91 (Cover date: May 2021), 107069.
  • Malathkar NV, Soni SK. High compression efficiency image compression algorithm based on subsampling for capsule endoscopy. Multimed Tools Appl. 2021;80:22163–22175. doi:10.1007/s11042-021-10808-0.
  • Devadoss CP, Sankaragomathi B. Near lossless medical image compression using block BWT–MTF and hybrid fractal compression techniques. Cluster Comput. 2019;22:12929–12937. doi:10.1007/s10586-018-1801-3.
  • Raja SP. Joint medical image compression–encryption in the cloud using multiscale transform-based image compression encoding techniques. Sādhanā. 2019;44:28, doi:10.1007/s12046-018-1013-9.
  • Xu, J., Mou, J., Liu, J. et al. The image compression–encryption algorithm based on the compression sensing and fractional-order chaotic system. Vis Comput (2021). doi:10.1007/s00371-021-02085-7.
  • Binu D, Kariyappa BS. “RideNN: a new rider optimization algorithm-based neural network for fault diagnosis in analog circuits,” IEEE Transactions on Instrumentation and Measurement.
  • Xin-She Yang, Mehmet Karamanoglu & Xingshi He, “Flower pollination algorithm: a novel approach for multiobjective optimization,” Engineering Optimization, 46:9, 1222–1237, 2014. doi:10.1080/0305215X.2013.832237.
  • Marsaline Beno M, Valarmathi IR, Swamy SM, et al. Threshold prediction for segmenting tumour from brain MRI scans. Int J Imaging Syst Technol. 2014;24(2):129–137. doi:10.1002/ima.22087.
  • http://www-cvr.ai.uiuc.edu/ponce_grp/data/: access data 2019-06-11.
  • http://www.ultrasoundcases.info/case-list.aspx?cat = 26: access date 2019-06-11.
  • Theodoros D. Vrionis, Xanthi I. Koutiva, and Nicholas A. Vovos, “A genetic algorithm-based low voltage ride-through control strategy for grid connected doubly fed induction wind generators,” IEEE Transactions on Power Systems, vol. 29, no. 3, May 2014.
  • Pedersen MEH, Chipperfield AJ. Simplifying particle swarm optimization. Appl Soft Comput. 2010;10(2):618–628.

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