Image Compression Based on Wavelet Transform

2014 ◽  
Vol 568-570 ◽  
pp. 749-752
Author(s):  
Chang Li Yan ◽  
Quan Cai Deng ◽  
Xin Zhang
Keyword(s):  
Image Processing ◽  
Wavelet Analysis ◽  
Image Compression ◽  
Image Coding ◽  
Two Dimensional ◽  
Wavelet Coefficients ◽  
Wavelet Theory ◽  
Two Dimensional Image ◽  
Ezw Algorithm ◽  
Selection Of

The wavelet analysis has some important applications in image processing, including image compression, image de-noising and so on.Wavelet analysis for two-dimensional image compression is a key aspect in the field of its applications.This paper studied the application of wavelet analysis in BMP image coding, the characteristics of wavelet coefficients and wavelet subimage, these lay the foundation for further selection of wavelet coefficients and optimize, and analyzed the theory of EZW algorithm, illustrates the better results of the applications on using wavelet theory in image processing.

scholarly journals Flexible and High Performance ASIPs for Pixel Level Image Processing and Two Dimensional Image Processing

2013 ◽  
Vol 21 (3) ◽  
pp. 552-562
Author(s):  
Hsuan-Chun Liao ◽  
Mochamad Asri ◽  
Tsuyoshi Isshiki ◽  
Dongju Li ◽  
Hiroaki Kunieda
Keyword(s):  
Image Processing ◽  
High Performance ◽  
Two Dimensional ◽  
Dimensional Image ◽  
Two Dimensional Image

Mycielski Based 2d-Predictive Image Coding Algorithm

2016 ◽  
Vol 850 ◽  
pp. 144-151 ◽  
Author(s):  
Mehmet Fidan ◽  
Ömer Nezih Gerek
Keyword(s):  
Image Coding ◽  
Predictive Coding ◽  
Prediction Method ◽  
Physical Distance ◽  
Prediction Algorithm ◽  
Two Dimensional ◽  
Two Dimensional Image ◽  
Past Data ◽  
And Performance ◽  
Definition Of

The Mycielski method is a prospering prediction algorithm which is based on searching and finding largest repeated binary patterns. It uses infinite-past data to devise a rule based prediction method on a time series. In this work, a novel two-dimensional (image processing) version of the Mycielski algorithm is proposed. Since the dimensionality definition of “past” data increases in two-dimensional signals, the proposed algorithm also needs to handle how the boundaries of the pixel cliques are iteratively extended in the neighborhood of a current pixel. The clique extension invokes novel similarity search strategies that depend on the chosen physical distance metric. The proposed prediction algorithm is used for predictive image compression and performance comparisons with other predictive coding methods are presented.

The VLSI design of a two dimensional image processing array

1984 ◽  
Vol 14 (3-4) ◽  
pp. 125-132 ◽  
Author(s):  
Dimokritos Panogiotopoulos ◽  
Nikolaos Bourbakis
Keyword(s):  
Image Processing ◽  
Vlsi Design ◽  
Two Dimensional ◽  
Dimensional Image ◽  
Two Dimensional Image

Simultaneous Least Squares Wavelet Decomposition for Multidimensional Irregularly Spaced Data

2012 ◽  
Vol 239-240 ◽  
pp. 1213-1218 ◽  
Author(s):  
Mehdi Shahbazian ◽  
Saeed Shahbazian
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Wavelet Decomposition ◽  
Least Square ◽  
Multidimensional Case ◽  
Discrete Wavelet ◽  
Two Dimensional ◽  
Wavelet Coefficients ◽  
Sampled Data ◽  
Irregularly Sampled Data

The multidimensional Discrete Wavelet Transform (DWT) has been widely used in signal and image processing for regularly sampled data. For irregularly sampled data, however, other techniques should be used including the Least Square Wavelet Decomposition (LSWD). The commonly used level by level (sequential) wavelet decomposition, which calculates the wavelet coefficients in each resolution separately, may result in a gross interpolation error. To overcome this drawback, a different approach called the Simultaneous Least Square Wavelet Decomposition, which computes all wavelet coefficients simultaneously, have been proposed by the authors. In this paper, we extend the simultaneous LSWD approach to the multidimensional case and show that this method has excellent reconstruction property for two dimensional irregularly spaced data.

On estimation of noise variance in two-dimensional signal processing

1991 ◽  
Vol 23 (3) ◽  
pp. 476-495 ◽  
Author(s):  
Peter Hall ◽  
J. W. Kay ◽  
D. M. Titterington
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Digital Signal ◽  
Optimal Selection ◽  
Two Dimensional ◽  
Noise Variance ◽  
Asymptotically Optimal ◽  
Extensive Analysis ◽  
Optimal Configurations ◽  
Selection Of

Estimation of noise variance is an important component of digital signal processing, in particular of image processing. In this paper we develop methods for estimating the variance of white noise in a two-dimensional degraded signal. We discuss optimal configurations of pixels for difference-based estimation, and describe asymptotically optimal selection of weights for the component pixels. After extensive analysis of possible configurations we recommend averaging linear configurations over a variety of different orientations (usually two or four). This approach produces estimators with properties of both statistical and numerical efficiency.

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