Abstract
Digital images are of a great importance in medicine. Efficient and compact storing of the medical digital images represents a major issue that needs to be solved. JPEG lossy compression algorithm is most widely used where better compression to quality ratio can be obtained by selecting appropriate quantization tables. Finding the optimal quantization tables is a hard combinatorial optimization problem and stochastic metaheuristics have been proven to be very efficient for solving such problems. In this paper we propose adjusted bare bones fireworks algorithm for quantization table selection. The proposed method was tested on different medical digital images. The results were compared to the standard JPEG algorithm. Various image similarity metrics were used and it has been shown that the proposed method was more successful.
M. Tuba—This research is supported by the Ministry of Education, Science and Technological Development of Republic of Serbia, Grant No. III-44006.
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References
Gupta, M., Garg, A.K.: Analysis of image compression algorithm using DCT. Int. J. Eng. Res. Appl. (IJERA) 2, 515–521 (2012)
Amri, H., Khalfallah, A., Lapayre, J.C., Bouhlel, M.S.: REPro. JPEG: a new image compression approach based on reduction/expansion image and JPEG compression for dermatological medical images. Imaging Sci. J. 65, 98–107 (2017)
Dubey, V.G., Singh, J.: 3D medical image compression using Huffman encoding technique. Int. J. Sci. Res. Publ. 2 (2012)
Cheng, C., Liu, Z., Hu, C., Meng, M.Q.H.: A novel wireless capsule endoscope with JPEG compression engine. In: IEEE International Conference on Automation and Logistics (ICAL), pp. 553–558 (2010)
Thai, T.H., Cogranne, R., Retraint, F.: JPEG quantization step estimation and its applications to digital image forensics. IEEE Trans. Inf. Forensics Secur. 12, 123–133 (2017)
Zimbico, A., Schneider, F., Maia, J.: Comparative study of the performance of the JPEG algorithm using optimized quantization matrices for ultrasound image compression. In: 5th ISSNIP-IEEE Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC), pp. 1–6 (2014)
Sabri, A.A., Sultan, N.H.: Quantization matrix for medical image compression using framelet transform. Kufa J. Eng. 5, 77–92 (2014)
Lazzerini, B., Marcelloni, F., Vecchio, M.: A multi-objective evolutionary approach to image quality/compression trade-off in JPEG baseline algorithm. Appl. Soft Comput. 10, 548–561 (2010)
Tuba, M., Bacanin, N.: JPEG quantization tables selection by the firefly algorithm. In: International Conference on Multimedia Computing and Systems (ICMCS), pp. 153–158. IEEE (2014)
Dua, R.L., Gupta, N.: Fast color image quantization based on bacterial foraging optimization. In: Fourth International Conference on Advances in Recent Technologies in Communication and Computing (ARTCom), pp. 100–102 (2012)
Viswajaa, S., Kumar, V., Karpagam, G.R.: A survey on nature inspired meta-heuristics algorithm in optimizing the quantization table for JPEG baseline algorithm. Int. Adv. Res. J. Sci. Eng. Technol. 2, 114–123 (2015)
Tan, Y., Zhu, Y.: Fireworks algorithm for optimization. In: Tan, Y., Shi, Y., Tan, K.C. (eds.) ICSI 2010. LNCS, vol. 6145, pp. 355–364. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13495-1_44
Li, J., Tan, Y.: The bare bones fireworks algorithm: a minimalist global optimizer. Appl. Soft Comput. 62, 454–462 (2018)
Tuba, E., Tuba, M., Dolicanin, E.: Adjusted fireworks algorithm applied to retinal image registration. Stud. Inf. Control 26, 33–42 (2017)
Tuba, E., Tuba, M., Simian, D.: Wireless sensor network coverage problem using modified fireworks algorithm. In: International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 696–701. IEEE (2016)
Tuba, E., Tuba, M., Beko, M.: Node localization in ad hoc wireless sensor networks using fireworks algorithm. In: 5th International Conference on Multimedia Computing and Systems (ICMCS), pp. 223–229. IEEE (2016)
Johnson, K.A., Becker, J.A.: The whole brain atlas, June 1999. http://www.med.harvard.edu/AANLIB/
Deeba, F.: Bleeding images and corresponding ground truth of CE images (2016). https://sites.google.com/site/farahdeeba073/Research/resources
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Tuba, E., Jovanovic, R., Beko, M., Tallón-Ballesteros, A.J., Tuba, M. (2018). Bare Bones Fireworks Algorithm for Medical Image Compression. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11315. Springer, Cham. https://doi.org/10.1007/978-3-030-03496-2_29
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