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What is wavelet transform coding? 

Discrete wavelet transform
Wavelet
Stationary wavelet transform
Wavelet packet decomposition
Wavelet transform

Answers from top 9 papers

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Papers (9)Insight
Journal Article•DOI
On the performance of linear phase wavelet transforms in low bit-rate image coding
E.A.B. da Silva, Mohammed Ghanbari 
01 May 1996-IEEE Transactions on Image Processing
61 Citations
Analysis of the data obtained strongly suggests that the design of good wavelet transforms for low bit-rate image coding should take into account chiefly the shape of the synthesis wavelet and, to a lesser extent, the coding.
Journal Article•DOI
Cyclic wavelet transforms for arbitrary finite data lengths
Sandip Sarkar, H. Vincent Poor 
01 Dec 2000-Signal Processing
13 Citations
This generalization is largely motivated in part by the need for such transforms for building error correcting codes in the wavelet transform domain.
Proceedings Article•DOI
ROI coding with integer wavelet transforms and unbalanced spatial orientation trees
S. Tasdoken, A. Cuhadar 
17 Sep 2003
10 Citations
In fact, this region-based representation retains the properties of the conventional wavelet transform, and thereby facilitates the use of conventional wavelet coefficient plane coders for region-based coding.
Open access•Journal Article•DOI
A modified wavelet energy rate-based damage identification method for steel bridges
Mohammad Noori, Haifegn Wang, Wael A. Altabey, Ahmad I. H. Silik 
01 Dec 2018-Scientia Iranica
28 Citations
The wavelet transform is a new way to analyze the signals, which is capable of providing multiple levels of details and approximations of the signal.
Proceedings Article•DOI
Quadtree-guided wavelet image coding
Chia-Yuan Teng, David L. Neuhoff 
31 Mar 1996
12 Citations
The result is a coding method with performance comparable to those of the best known wavelet coders, but with less complexity.
Open access•Journal Article•DOI
Generalized Daubechies Wavelet Families
Cedric Vonesch, Thierry Blu, Michael Unser 
01 Sep 2007-IEEE Transactions on Signal Processing
189 Citations
This allows one to tune the corresponding wavelet transform to a specific class of signals, thereby ensuring good approximation and sparsity properties.
Journal Article•DOI
A binary wavelet decomposition of binary images
M.D. Swanson, Ahmed H. Tewfik 
01 Dec 1996-IEEE Transactions on Image Processing
101 Citations
In particular, it yields an output similar to the thresholded output of a real wavelet transform operating on the underlying binary image.
Open access•Proceedings Article•DOI
Wavelet-based contourlet transform and its application to image coding
R. Eslami, Hayder Radha 
24 Oct 2004
140 Citations
Our simulation results also show that this new coding approach is competitive to the wavelet coder in terms of the PSNR-rate curves, and is visually superior to the wavelet coder for the mentioned images.
Open access•Journal Article•DOI
An Algorithm of Daubechies Wavelet Transform in the Final Field When Processing Speech Signals
Dmitry Popov, Artem Gapochkin, Alexey Nekrasov 
18 Jul 2018-Electronics
15 Citations
In addition, modular codes make it possible to implement large-scale signal processing using the wavelet transform.

Related Questions

What is wavelet tranform?4 answersWavelet transform is a time-frequency analysis method that divides a signal into sub-band signals of different frequency bands. It has the ability to represent the local characteristics of signals in both time and frequency domains, making it useful for analyzing signals. The selection of the basic wavelet is important for its effectiveness. The wavelet transform has advantages over other time-frequency representations, such as the short-time Fourier transform, and has gained popularity in recent years. It is used in various applications, including EEG signal analysis for noise removal, spectrum sensing in cognitive radio networks, and processing gamma-ray spectra for peak detection and background removal. The wavelet transform is a versatile tool that finds applications in signal processing, compression, and other fields.
How can deep learning models be used to learn wavelet packet transforms?4 answersDeep learning models can be used to learn wavelet packet transforms by incorporating the wavelet packet transform (WPT) as a preprocessing step before applying the deep learning model. One approach is to use the WPT to extract time-frequency domain information from the data, which is then fed into a convolutional neural network (CNN) for feature extraction and classification. Another approach is to use the maximum overlap discrete wavelet transform (MODWT) to decompose the input variables and explore the impact of wavelet transform in improving the simulations. Additionally, the fast wavelet transform can be applied to compress linear layers in neural networks, allowing for efficient representation of the linear layers with significantly fewer parameters.
How do you create a wavelet in Python?9 answers
How do you read a wavelet power spectrum?10 answers
How do you use a wavelet analyzer in Matlab?8 answers
What are DSP wavelets?17 answers

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