A Metaheuristic Approach for Tetrolet-Based Medical Image Compression

Saravanan S., Sujitha Juliet

Source Title: Journal of Cases on Information Technology (JCIT)24(2)

ISSN: 1548-7717|EISSN: 1548-7725|EISBN13: 9781799878216|DOI: 10.4018/JCIT.20220401.oa3

MLA

Saravanan S., and Sujitha Juliet. "A Metaheuristic Approach for Tetrolet-Based Medical Image Compression." JCIT vol.24, no.2 2022: pp.1-14. http://doi.org/10.4018/JCIT.20220401.oa3

APA

Saravanan S. & Juliet, S. (2022). A Metaheuristic Approach for Tetrolet-Based Medical Image Compression. Journal of Cases on Information Technology (JCIT), 24(2), 1-14. http://doi.org/10.4018/JCIT.20220401.oa3

Chicago

Saravanan S., and Sujitha Juliet. "A Metaheuristic Approach for Tetrolet-Based Medical Image Compression," Journal of Cases on Information Technology (JCIT) 24, no.2: 1-14. http://doi.org/10.4018/JCIT.20220401.oa3

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Abstract

Over recent times, medical imaging plays a significant role in clinical practices. Storing and transferring the huge volume of images becomes complicated without an efficient image compression technique. This paper proposes a compression algorithm that uses a Haar based wavelet transform called Tetrolet transform, which reduces the noise on the input images and decomposes with a 4 x 4 blocks of equal squares called tetrominoes. It opts for a decomposing using optimal scheme for achieving the input image into a sparse representation which gives a much-detailed performance for texture and edge information better than wavelet transform. Set Partitioning in Hierarchical Trees (SPIHT) is used for encoding the significant coefficients to achieve efficient image compression. It has been investigated with various metaheuristic algorithms. Experimental results prove that the proposed method outperforms the other transform-based compression in terms of PSNR, CR, and Complexity. Also, the proposed method shows an improved result with another state of work.