Medical Image Compression using DCT with Entropy Encoding and Huffman on MRI Brain Images

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dc.contributor.authorJacob, Rani
dc.date.accessioned2025-07-24T16:23:01Z
dc.date.available2025-07-24T16:23:01Z
dc.date.issued2022
dc.description.abstractDigital image compression is a modern technology which comprises of wide range of use in different fields as in machine learning, medicine, research and many others. Many techniques exist in image processing. This paper aims at the analysis of compression using Discrete Cosine Transform (DCT) by using special methods of coding to produce enhanced results. DCT is a technique or method used to transform pixels of an image into elementary frequency component. It converts each pixel value of an image into its corresponding frequency value. There has to be a formula that has to be used during compression and it should be reversible without losing quality of the image. These formulae are for lossy and lossless compression techniques which are used in this project. The research test Magnetic Resonance Images (MRI) using a set of brain images. During program execution, original image will be inserted and then some algorithms will be performed on the image to compress it and a decompressing algorithm will execute on the compressed file to produce an enhanced lossless image.
dc.identifier.issn2456-883X
dc.identifier.urihttps://ds.dmiseu.org/handle/123456789/91
dc.language.isoen
dc.publisherAsian Journal of Applied Science and Technology (AJAST)
dc.subjectImage compression
dc.subjectDiscrete cosine transform
dc.subjectMagnetic resonance images
dc.subjectEntropy.
dc.titleMedical Image Compression using DCT with Entropy Encoding and Huffman on MRI Brain Images
dc.typeArticle

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