Comparison Methods of DCT, DWT and FFT Techniques Approach on Lossy Image Compression
Authors: Fifit Alfiah, Saepudin, Sutarya, Indra Purnama, Ryan Anggara
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This paper presents a study of image compression methods algorithm for compare the best techniques on lossy image compression. One of the major difficulties encountered in compression for lossy imagethat how to shield quality of image in a way that the compressed image constantlyidentical to the authentic, different from the types of methods that exist in the lossless image that can maintain the quality of the images authenticity. In compressing images there are also many methods that can be used with various algorithms such as Huffman code, Chandhuri and Hocquengham (BCH) Codes, Multiple-Tables Arithmetic Code, Fractal Coding, Block Truncation Coding and many other algorithms. In Transform domain, the image is for gain a rarely coefficient matrix using DWT, DCT and FFT. DCT method is almost similar to discrete Fourier transform (DFT), which works to convert a signal or image by a spatial domain into a frequency domain. Because amount variety of images, Binary Image, RGB image, image intensity. Then adopt compressing an image to decrease the size of the data ortechniques to subtract the amount of bits required to reflect an images. Based on the results of this study using 3 methods that can perform compression image with different compression results with each method (DCT, FFT and DWT) as much as 4 variations of measurement percentage of image compression of each method starting from compressing image with size 10%, 30%, 50% and 70%. Comparing the three methods with four different variations of presentation measurement can give very accurate and clear results which method is best for compression the image of the present percentage size.