Jacob, Rani2025-07-242025-07-2420222249-7455https://ds.dmiseu.org/handle/123456789/92The growing demand for medical image storage and transmission has resulted in a shortage of memory and bandwidth. Compression was used to find these issues. Clinical image compression is used to improve image quality, reduce bit rate, increase compression efficiency for storage and transmission, and reduce cost. MRI images are quite clinical. To store and transmit thousands of MRI pictures, you need a lot of storage space and bandwidth. Thus, high-quality MRI image compression is more research focused. Many Compression strategies for MRI with low compression rate cause loss of data on lesions, and lead to misdiagnose. This research proposed several MRI image compression methods. Our main goals are to provide more compressed clinical images, encourage early location and finding followed by therapy using multi-resolution compression technology. A two-dimensional (2D) picture arrangement is created by first converting 3D MRI scans into 2D images. Then range and area blocks are arranged by 3D object's spatio-temporal similarity. In addition, the proposed technique uses wavelet transform and MRG algorithm to analyse the performance of wavelet contains transformation, quantization, and entropy coding to compress the most significant piece of ROI using DWT and s. It compresses non-ROI using DCT and MHE (merging based Huffman encoding). Finally, residual compensation is used to provide good decompression quality MRI compression.enImage ProcessingMRICompressionImages.Article