How can a DCT help identify the obvious frequency of a discrete wave?

In summary, the conversation discusses the use of DCT and a pass filter to identify the "obvious" frequency of a discrete wave. It is suggested to use a full Fourier Transform instead of just a DCT for better results.
  • #1
yetar
54
0
Hello,

Suppose I have a discrete function of a perfect cosine wave.
So if I will do a DCT on this function I will get one non zero coefficient which corresponds to the perfect cosine wave, and the rest will be zero.
Now I have a pass filter, which filters out anything with a frequency which is different from the original cosine wave.
If I will do this filter on the DCT I did to the cosine wave, then no coefficient should change.
Now, suppose I have a second function which is also a perfect cosine wave of the same frequency as the cosine wave in the first function, but with a different phase.
So the DCT of the second function will give me many non zero coefficients.
If will pass the same filter I did on the first function DCT, then I will loose many coefficient and the result will be some wave which is weaker then the second function original cosine wave.
Is that true?

Basicaly I am trying to find the "obvious" frequency of a discrete wave.
Lets say I have a pure triangle wave. Doing DCT on it will produce a lot of coefficients of different frequencies, but how do I discover the obvious frequency of the triangle wave from these coefficients?

Thanks in advance.
 
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  • #2
The problem is that you are confining yourself to a DCT. Do a full Fourier Transform instead.
 

What is Discrete Cosine Transform?

Discrete Cosine Transform (DCT) is a mathematical technique used for analyzing and compressing data, particularly in signal processing and image compression. It converts a signal or image from its original spatial domain to a frequency domain representation, resulting in a smaller set of coefficients that can be used to reconstruct the original data with minimal loss of information.

How is Discrete Cosine Transform different from Fourier Transform?

DCT is similar to Fourier Transform in that it also converts data from the spatial domain to the frequency domain. However, DCT is specifically designed for real-valued data and is more efficient in compressing data with a high degree of symmetry. Unlike Fourier Transform, DCT produces a real-valued output, making it more useful for practical applications such as image and audio compression.

What are the applications of Discrete Cosine Transform?

DCT has various applications in signal and image processing, including data compression, data hiding, and image enhancement. It is commonly used in video and image compression standards such as JPEG, MPEG, and H.264. DCT is also used in audio compression standards such as MP3 and AAC.

How does Discrete Cosine Transform help in data compression?

Discrete Cosine Transform is used in data compression because it reduces the amount of data needed to represent a signal or image. It does this by identifying the most significant frequencies in the original data and discarding the less important ones. The resulting smaller set of coefficients can then be used to reconstruct the original data with minimal loss of information, resulting in a more efficient compression.

What are the advantages of Discrete Cosine Transform?

One of the main advantages of DCT is its ability to compress data with a high degree of symmetry, such as images and audio. It is also computationally efficient, making it suitable for real-time applications. Additionally, DCT is reversible, meaning the original data can be reconstructed from the compressed data, making it a lossy compression method.

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