## 1 Introduction
Medical image compression primarily aims to reduce the image size to minimize the memory required for processing and storing the image without affecting its quality. Even though considerable research strategies for medical image compression were developed, an algorithm specific to medical image compression that focuses on reducing computational speed, area, and power has not been developed. The digital compression of medical images, especially for telemedicine, has gained significant importance in recent years due to medical advancements. Primary health care, especially in villages, is challenging owing to the limited facilities available and the lack of well-trained health personnel. With telemedicine, most of the problems faced by people living in villages can be solved. The main idea behind telemedicine is the efficient transmission of medical images to healthcare professionals in modern cities. A critical factor that comes into play in telemedicine is to handle the transmission of cumbersome medical images with comparatively lower bandwidth. This demands that the medical images be compressed to enable the efficient and error-free transmission of information. This research explores the DCT that can be utilized for digital image compression by incorporating the computational unit to reduce computational complexity and minimize power as well as area. In the DCT coding technique followed in this research, a considerable set of highly correlated pixels are transformed to a comparatively smaller set of transform coefficients that are decorrelated. The quantization and encoding of the coefficients are then performed to obtain the compressed image. This research aims to design and develop a computational unit for medical image compression comprising a modified Carry Select Adder (CSLA)-based Selector-Based Error Tolerant Adder (SBETA)/Low-Power Selector-Based Error Tolerant Adder (LPS-BETA)/Optimized Error Tolerant Adder (OETA) using RCA, a divide and conquer multiplier, a PDP optimized multiplier using iterative carry save SBETA and a logic unit using operand isolation. In the proposed research work, an architecture of a computational unit incorporated in the Loeffler 8-point DCT/IDCT algorithm and used for medical image compression applications is proposed, designed, and implemented. The ET adders designed using the low-power concept of approximate computing substitute the addition operations involved in [PERSON]'s fast algorithm's DCT/IDCT computations. This reduces the hardware complexity, resulting in significant savings in area and power. The Binary to Excess 1 Converters (BEC) used in the modified CSLA reduces the number of gates involved, leading to lesser power and area as the transitions rely on only one bit. The multiplications involved are minimized by approximating some of the cosine constants involved and using the proposed low-power multipliers and shifters to reduce the computational complexity. An efficient encoding and decoding block is proposed using the improved Loeffler algorithm. The contributions of this research are as follows:  
1. A DCT/IDCT architecture based on the Loeffler algorithm by incorporating the computational unit to perform the additions involved in the butterfly diagram using the ET adders and the low-power multipliers for the multiplications involved. 2. An improved encoding and decoding block is used to reduce the circuit complexity and improve the compression ratio, SSIM, and PSNR without affecting the quality of the reconstructed image. 3. An approach to building a computational unit by integrating VLSI ASIC design and the concepts of low-power VLSI compatible for medical image compression used in telemedicine was adopted in this research. The VLSI ASIC design flow that is followed in this research work is presented in Section 2. Section 3 elaborates on the development of the proposed computational unit designed and implemented in this research.