Open AccessComparison of discrete cosine transform and dual-tree complex wavelet transform based on arithmetic coding in medical image compression
Author(s) -
Ledya Novamizanti,
Anggunmeka Luhur Prasasti,
Ilham Faezar Noor Kiranda
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1367/1/012021
Subject(s) - discrete cosine transform , complex wavelet transform , image compression , arithmetic coding , transform coding , artificial intelligence , mathematics , discrete wavelet transform , quantization (signal processing) , entropy encoding , computer science , data compression , wavelet transform , arithmetic , algorithm , computer vision , pattern recognition (psychology) , image processing , wavelet , context adaptive binary arithmetic coding , image (mathematics)
Medical image contains very important information in the medical world used to diagnose diseases by doctors. Storage for storing medical imagery requires large storage media. The difficulty of sending and storing files, hence the process of compression image that can shrink the size of medical image. This research uses the Discrete Cosine Transform and Dual-Tree Complex Wavelet Transform-based Arithmetic Coding. DCT is used in the process of compression of imagery files using the cosine value approach. In DCT, there is a quantization process that causes image quality to decline, as it eliminates some information on imagery. DTCWT uses 2 trees, where tree 1 is for real value and tree 2 for imaginary value. Arithmetic Coding is one of entropy encoding that uses the range and probability values in the calculation. The value of decoding results in Arithmetic Coding has the same value as the original image. DCT produce a higher compression ratio than DTCWT, while DTCWT get higher PSNR than DCT.