Lossy medical image compression using Huffman coding and singular value decomposition

Medical images generate enormous amounts of data and therefore, efficient image compression techniques need to be employed in order to save on cost and time of storage and transmission respectively. In this research work, we propose a new lossy compression technique by using singular value decomposition (SVD) followed by Huffman coding. In the proposed technique firstly the image is decomposed by using SVD and then the rank is being reduced by ignoring some of the lower singular values as well as rows of hanger and aligner matrices. Then the reconstructed lossy image is being compressed again by using Huffman coding. The compression ratio is obtained by multiplication of the compression ratio achieved by using SVD with the compression ratio achieved by using Huffman coding. The proposed technique is tested on several medical images. The obtained results were also compared with those of conventional Huffman coding and JPEG2000. The quantitative and visual results are showing the superiority of the proposed compression technique over the aforementioned compression technique.