Relation between entropys of spatial and frequency domain

AI Thread Summary
The discussion centers on measuring the entropy of visual data in both spatial and frequency domains, specifically using DCT for frequency analysis. It highlights a discrepancy between entropy values obtained from pixel data in the spatial domain and those derived from DCT coefficients. The original poster seeks to understand the relationship between these two entropy measurements, referencing Parseval's theorem but finding no clear connections in existing literature. Participants are encouraged to provide insights or references that clarify this relationship. The conversation emphasizes the need for a deeper understanding of entropy in different domains for effective image and video analysis.
Chriszz
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Dears,

I wish to measure the entropy of visual data such as image and video.
I did transform these image and videos into frequency domain such as DCT, then measured the entropy with these pdfs for each AC components of image and videos.

However, when I measured the entropy over spatial domain such as pixel, this entropy is differ to the entropy from DCT domain.

What is the relationship between the entropy of spatial and frequency domains? (Such as Parseval's theorem)
I couln't find this relation in the textbook for signal processing or the google.

Please give a comments for this relation.

Thanks!
 
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