Discrete Wavelets - Question on decimation of high pass

In summary, the conversation discusses the concept of discrete wavelets and the process of decomposing a signal using wavelet filter banks. The high pass and low pass filters are both decimated by 2, and while it may seem counterintuitive to decimate the high pass output, this is possible because the wavelet filters used are designed to have a finite bandwidth. This means that even when downsampling, the high frequency information is not lost because the cutoff frequency of the high pass filter is still above the highest frequency in the signal.
  • #1
TheWang
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Hi, I'm trying to grasp the concept of discrete wavelets and can't seem to find an answer to my question.
In the decomposition of a signal using wavelets filter banks, the signal goes through a low pass and high pass filter. The output of the low pass and high pass is decimated by 2. I can understand the low pass output being decimated by 2 since only the lower half of the bandwidth is kept. However, how is it possible to decimate the high pass output by 2 given Nyquist theory? I'm imagining if the original signal was sampled at a rate slightly higher than twice the highest frequency in the signal, the output of the high pass filter bank can't be downsampled without losing the high frequency information. Can anyone help me see this clearly? Thanks alot
 
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  • #2
! The key to understanding the decimation of the high pass output is to understand that the wavelet filters used in the decomposition of a signal are designed to be bandpass filters, meaning they have a finite bandwidth. This means that the high pass filter will only pass frequencies above a certain cutoff frequency. The cutoff frequency of the high pass filter is determined by the sampling rate of the original signal. So, when the signal is downsampled, it is still possible to discard the lower half of the bandwidth of the high pass filter without losing any of the high frequency information because the highest frequency in the signal will still be above the cutoff frequency of the high pass filter.
 

1. What is the purpose of decimating a high pass filter in discrete wavelet analysis?

Decimation of a high pass filter in discrete wavelet analysis is done in order to reduce the number of data points or samples in the signal. This helps to make the analysis more efficient and can also improve the accuracy of the results.

2. How is decimation of a high pass filter different from downsampling?

The terms decimation and downsampling are often used interchangeably, but there is a subtle difference between the two. Decimation specifically refers to reducing the number of samples in a signal by an integer factor, while downsampling can involve any reduction in sample rate, even if it is not by an integer factor.

3. Can decimation of a high pass filter affect the frequency response of the filter?

Yes, decimation of a high pass filter can affect its frequency response. When decimating by a factor of M, the frequency response of the filter will be compressed by a factor of M. This can result in a loss of high frequency information and a distortion of the original signal.

4. Is decimation of a high pass filter reversible?

No, decimation of a high pass filter is not reversible. Once a signal has been decimated, the information lost during the process cannot be recovered. Therefore, it is important to carefully consider the decimation factor and the potential loss of information before applying this process.

5. Are there any alternative methods to decimation for reducing the number of samples in a signal?

Yes, there are alternative methods to decimation for reducing the number of samples in a signal. One method is interpolation, which involves estimating new data points between existing samples. Another method is subsampling, which involves selecting only certain samples from the original signal for further analysis. These methods may be more appropriate depending on the specific application and desired results.

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