IntOPMICM: Intelligent Medical Image Size Reduction Model

Autor: Piyush Kumar Pareek, Chethana Sridhar, R. Kalidoss, Muhammad Aslam, Manish Maheshwari, Prashant Kumar Shukla, Stephen Jeswinde Nuagah
Rok vydání: 2022
Předmět:
Zdroj: Journal of Healthcare Engineering. 2022:1-11
ISSN: 2040-2309
2040-2295
DOI: 10.1155/2022/5171016
Popis: Due to the increasing number of medical imaging images being utilized for the diagnosis and treatment of diseases, lossy or improper image compression has become more prevalent in recent years. The compression ratio and image quality, which are commonly quantified by PSNR values, are used to evaluate the performance of the lossy compression algorithm. This article introduces the IntOPMICM technique, a new image compression scheme that combines GenPSO and VQ. A combination of fragments and genetic algorithms was used to create the codebook. PSNR, MSE, SSIM, NMSE, SNR, and CR indicators were used to test the suggested technique using real-time medical imaging. The suggested IntOPMICM approach produces higher PSNR SSIM values for a given compression ratio than existing methods, according to experimental data. Furthermore, for a given compression ratio, the suggested IntOPMICM approach produces lower MSE, RMSE, and SNR values than existing methods.
Databáze: OpenAIRE
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