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Saputro Febri
Department of Informatics, Faculty of Information Technology and Electrical Engineering, University of Technology Yogyakarta, Indonesia
Economics, Econometrics & Finance

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Wavelet-Walsh, Quantization, and Fractal Coding Transformation Methods to Minimize Image Data Size Asriningtias Yuli; Saputro Febri; Setyaningsih Emy
International Journal of Applied Business and Information Systems Vol. 2 No. 2 (2018)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (372.128 KB) | DOI: 10.31763/ijabis.v2i2.217

Abstract

Digital technology development requires an efficient and fast process, not only in the data transmission but also in the data saving. The digital image is one of the data which is commonly exchanged. However, image data with good quality has rather big size. So that they need to minimize data size to be efficient and fast is badly needed. One of the ways which can be done is to minimize the image data size is data compression. Data compression can be done by minimizing image data redundancy by minimizing the missing information in the digital image. This research proposed the mixing of Wavelet-Walsh, Quantization, and Fractal Coding Transformation Methods to compress image data greyscale so that it has a smaller size but still with good image quality. The test result showed that the average Compression Ratio is 1.32 with the decent reconstruction image result quality that is average value PSNR = 43.31 dB
Co-Authors Emy Setyaningsih Yuli Asriningtias