Access the full text.
Sign up today, get DeepDyve free for 14 days.
J.R. Dorp, S. Kotz (2002)
The standard two-sided power distribution and its properties: with applications in financial engineeringAm. Stat., 56
E. Lam (2004)
Analysis of the DCT coefficient distributions for document codingIEEE Signal Processing Letters, 11
A. Prudnikov, Yu. Brychkov, O. Marichev, R. Romer (1992)
Integrals and series
R. Reininger, J. Gibson (1983)
Distributions of the Two-Dimensional DCT Coefficients for ImagesIEEE Trans. Commun., 31
A.K. Jain (1989)
Fundamentals of Digital Image Processing
Zhong-De Wang, B. Hunt (1985)
The discreteW transformApplied Mathematics and Computation, 16
S. Nadarajah, S. Kotz (2006)
On the DCT Coefficient DistributionsIEEE Signal Processing Letters, 13
J. Dorp, S. Kotz (2002)
A novel extension of the triangular distribution and its parameter estimationThe Statistician, 51
P. Duhamel, M. Vetterli (1990)
Fast Fourier transforms: a tutorial review and a state of the art
J.W. Tukey (1977)
Exploratory Data Analysis
N. Ahmed, T. Natarajan, K. Rao (2019)
Discrete Cosine TransformIEEE Transactions on Computers, C-23
E. Lam, J. Goodman (2000)
A mathematical analysis of the DCT coefficient distributions for imagesIEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 9 10
K. Rao, P. Yip (1990)
Discrete Cosine Transform - Algorithms, Advantages, Applications
J. Dorp, S. Kotz (2002)
The Standard Two-Sided Power Distribution and its PropertiesThe American Statistician, 56
I. Gradshteyn, I. Ryzhik, A. Jeffrey, Y. Geronimus, M. Tseytlin, Y. Fung (1966)
Table of Integrals, Series, and ProductsJournal of Lubrication Technology, 98
E. Lam (2004)
Statistical modelling of the wavelet coefficients with different bases and decomposition levels, 151
D. Teichroew (1957)
The Mixture of Normal Distributions with Different VariancesAnnals of Mathematical Statistics, 28
It has been known that the distribution of the discrete cosine transform (DCT) coefficients of most natural images follow a Laplace distribution. However, recent work has shown that the Laplace distribution may not be a good fit for certain type of images and that the Gaussian distribution will be a realistic model in such cases. Assuming this alternative model, we derive a comprehensive collection of formulas for the distribution of the actual DCT coefficient. The corresponding estimation procedures are derived by the method of moments and the method of maximum likelihood. Finally, the superior performance of the derived distributions over the Gaussian model is illustrated. It is expected that this work could serve as a useful reference and lead to improved modeling with respect to image analysis and image coding.
Acta Applicandae Mathematicae – Springer Journals
Published: Sep 11, 2008
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.