Wavelet-Walsh, Quantization, and Fractal Coding Transformation Methods to Minimize Image Data Size

Asriningtias Yuli (Department of Informatics, Faculty of Information Technology and Electrical Engineering, University of Technology Yogyakarta, Indonesia)
Saputro Febri (Department of Informatics, Faculty of Information Technology and Electrical Engineering, University of Technology Yogyakarta, Indonesia)
Setyaningsih Emy (Faculty of Applied Science Science and Technology, Institute AKPRIND Yogyakarta, Indonesia)

International Journal of Applied Business and Information Systems
Vol. 2 No. 2 (2018)

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