Image Compression Based on Improved FFT Algorithm

Abstract

Image compression is a crucial step in image processing area. Image Fourier transforms is the classical algorithm which can convert image from spatial domain to frequency domain. Because of its good concentrative property with transform energy, Fourier transform has been widely applied in image coding, image segmentation, image reconstruction. This paper adopts Radix-4 Fast Fourier transform (Radix-4 FFT) to realize the limit distortion for image coding, and to discuss the feasibility and the advantage of Fourier transform for image compression. It aims to deal with the existing complex and time-consuming of Fourier transform, according to the symmetric conjugate of the image by Fourier transform to reduce data storage and computing complexity. Using Radix-4 FFT can also reduce algorithm time-consuming, it designs three different compression requirements of non-uniform quantification tables for different demands of image quality and compression ratio. Take the standard image Lena as experimental data using the presented method, the results show that the implementation by Radix-4 FFT is simple, the effect is ideal and lower time-consuming

New Algorithm for Dht and Its Verilog Implementation

“…In the next cycle another set of coefficients are obtained X [1], X [5], X [9] and X [13] depending on the value of p and q. Similarly, all the coefficients can be obtained in the consecutive clock cycles.…”

“…Four structures are implemented in parallel such that each unit gives 4 coefficients for N=16. For input sequence X = [1,7,8,9,3,2,4,5,1,5,3,4,5,6,7,8], outputs Y = [78, 13.6758, -0.3431, -9.9507, -18, -3.9761, -6, -1.2797, -14, -3.5337, -11.6568, 5.4654, -6, -14.1660, -6, 13.7650] were obtained as seen in the simulation diagram in figure 3 . Total of 45 clock cycles were required to obtain all 16 coefficients at output.…”

Pre and Postprocessing for JPEG to Handle Large Monochrome Images

“…Image compression algorithms can be categorized into either lossless or lossy [1,3]. While lossless compression methods conserve the original image to be recovered completely after the decompression process [4], lossy compression uses the inherent redundancies found in an image, such as inter-pixel redundancy, psycho-visual redundancy, or coding redundancy, to decrease the data amount needed to represent the image [5][6][7]. Accordingly, lossless methods produce low compression ratio and error-free images; meanwhile, lossy methods produce high compression ratio with additional error (PSNR) [8].…”

“…In spatial domain, image compression techniques aim to reduce the number of pixels representing the image without influencing the quality of the resulted image [9][10][11]. In frequency domain, Discrete Cosine Transform (DCT) [12,13], Discrete Fourier transform, or Discrete Wavelet Transform [5,14,15] are used to represent the energy of the image into a few number of coefficients. JPEG is the most widely used method for lossy compression of digital photographs.…”

Interleaved reception method for restored vector quantization image

“…Since the degradation will be in form of blocks. Many of published papers try to solve this problem by combination between the VQ and different transforms as shown in articles [4][5][6]. These combinations of those relatively basic methods utilize the favorable characteristics of each method [7].…”