## 1 Introduction
The growing demand for efficient transmission and storage of medical images at a reduced computational complexity in the medical field is the driving force of research designing a computational unit for image compression. The compression of medical images based on transforms has recently resulted in a huge demand due to their growing applications in the medical domain, attracting researchers. The efficiency and computational speed of Discrete Cosine Transform (DCT) are among the most sought-after algorithms, especially for medical images. Some of the DCT algorithms used in different medical image compression applications are listed here to give a clear idea about the existing work. An image compression algorithm using DCT was proposed by [PERSON] et al. [18], and the significant coefficients were obtained using adaptive sampling techniques. The proposed algorithm resulted in compressing the images and preserving the information fidelity. [PERSON] et al. [19] proposed a Two-Dimensional (2D) image compression method for medical images utilizing DCT and hierarchical tree partitioning. An approach with a reduced bit rate, resulting in minimizing the memory requirement for storing the remote diagnosis and for the rapid transmission, was also adopted in this work. An approximation technique for 8-point DCT with 14 addition operations utilizing the separability property of DCT was presented by [PERSON] et al. [20]. A reduction in the algorithm and computational complexity was observed. [PERSON] et al. [21] have proposed context tree lossless compression for the Region Of Interest (ROI) image part and a lossycompression fractal for the non-ROI part of the image. The proposed method provided much faster and more accurate results even though the PSNR was slightly lower. The research carried out by [PERSON] et al. [22] focused on the utilization of the UT sutra in the design of the multiplier that was incorporated into the image compression algorithm. A significant reduction in the size of the image was observed with less data loss. The compression speed was improved with the use of the Vedic multiplier as the computation time of the image compression algorithm was reduced. In their work, [PERSON] et al. [23] discussed the different standards used in image compression, mainly for medical images. A comparison of the performances of the different compression standards on publicly available datasets was also conducted. A DCT architecture without multipliers was proposed for medical applications targeting capsule endoscopy by [PERSON] et al. [24]. Approximate adders, threshold setting, and coefficient optimization were incorporated into the DCT algorithm design. A DCT architecture utilizing inexact adders was proposed by [PERSON] et al. [25]. Considerable savings in area and power were observed due to the absence of multipliers in the architecture. [PERSON] et al. [26] presented a 2D medical image compression algorithm in their work. The algorithm proposed exhibited high compression rates and good image resolution. [PERSON] et al. [27], in their work, compared two lossless transforms, DCT and Haar Wavelet Transform (HWT), with medical images in terms of image size and compression ratio. It was observed from their inferences that while using grayscale images, DCT proved to perform much better than HWT. HWT was found to be a better choice when color images were used and in terms of compression time. A discussion on image compression algorithms using DWT and Huffman coding was presented by [PERSON] et al. [28] in their work. In their work, [PERSON] et al. [29] analyzed and evaluated the strength of different lossless compression techniques based on more than one parameter. In an attempt to improve the compression ratios, the medical image compression techniques discussed resulted in a reduced quality of images and loss of minute details, which is undesirable in medical images.