Abstract
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.
Highlights
Various traditional approaches such as JPEG [9], JPEG 2000 [10], SPHIT [11], EBCOT [12], and lifting scheme [13] have been developed for medical image compression, as well as current approaches such as artificial intelligence-based approaches such as neural network-based approaches
As compared to JPEG 2000, this compression approach produced higher-quality compressed images at high decomposition levels. e authors of [24] suggested an image compression technique based on artificial neural networks, in which the original image was coded in terms of both pixel coordinates and pixel values
To control the output of the hidden layer, we need to combine the output with other compression techniques. is method is proposed as a hybrid approach that combines neural networks and GenPSO
Summary
Various traditional approaches such as JPEG [9], JPEG 2000 [10], SPHIT [11], EBCOT [12], and lifting scheme [13] have been developed for medical image compression, as well as current approaches such as artificial intelligence-based approaches such as neural network-based approaches. E authors of [20] proposed an image compression technique for medical images that used wavelet and radial basis function neural network (RBFFN) features, as well as vector quantization. E authors of [24] suggested an image compression technique based on artificial neural networks, in which the original image was coded in terms of both pixel coordinates and pixel values. E authors of [26] suggested a medical image compression method based on the radial basis function of a neural network. Ese procedures are followed to make a codec with GenPSO: the GenPSO structure uses a method of encoding real numbers and sticks to chromosomal codes as a single solution It generates the first population based on the picture block’s vector training. E first step in the process is to determine the starting population of PSO in the hybrid approach to maximize the efficiency of the parameter network. e solution is divided into two groups, namely the first group of PSOs and the second group of PSOs
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