How to know what pixels where compressed and not compressed in a image?

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Hudson Romualdo 2022-11-28

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评论： William Rose 2022-11-30

Havin a image (jpg or png - I can have lots of extensions) it's possible to know what them was compressed?

For exemple, let's say that I have a 1280 x 720 image. Than in a uncompressed image I will have a 1280 x 720 matrix of int values representing the image.

But if the same image is compressed (if possible), will be less then that.

My goal: I need to process a image (compressed) and have a vector of ones and zeros representing their pixels. The ones means that the pixel in that position it's important and wasn't compressed. Otherwise, zeros means that this particular pixel is not important and was compressed.

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Answer 1

William Rose 2022-11-28

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@Hudson Romualdo,

It is not true that specific pixels are compressed or not compressed whn an image is compressed. The JPEG algorithm does a color transofrm on the entire eimage, then the chrominance component of color (at every pixel) is downlsized. This means that the color information at each pixel is now more coarse than originally. Then the image is divided into 8x8 pixlel cheunks, and each chuink is processed with a dicrete cosine transoform, and discarding the higher frequencies from each 8x8 black. Therefore this comptession step also does not keep or reject specific pixels. The last compression step, Huffman coding, also compresses in a way that is not pixel-specific.

See http://pi.math.cornell.edu/~web6140/TopTenAlgorithms/JPEG.html.

PNG and other compression algorithms are also complex, like JPEG, and do not correspond to keeping or rejecting specific pixels.

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William Rose 2022-11-29

@Hudson Romualdo,

The paper "Fdereated Deep Learning..." by M. Chen et al. includes the sentences you quote. They are not discussing a specific compression algorithm. They are describing a model of information transmission in virtual reality applications. They want to understand the factors that result in breaks in presence in the VR experience, and they want to understand the statistical distribution of breaks in presence, as a function of wireless network performance and other factors. Their model includes the idea that some pixels are more important than others for the VR experience. The loss of highly important pixels is more likely to cause a break in VR presence than the loss of unimportant pixels.

The authors say "a number of pixels can be compressed at the BS and recovered by the user. Hence, the pixels that can be compressed by the BS [are] not important. However, some of the pixels cannot be compressed by the BS and, hence, they need to transmit to the VR users."

I think the authors' description of image compression is not entirely accurate. However, it is probably true that some pixels are more important than others for maintaining virtual reality presence.

William Rose 2022-11-29

@Hudson Romualdo, WHich formula in the M. Chen et al (2019) paper (here and here) are you trying to implement? Equaitons 7 and 8 constitute their model of the statical distribution of breaks in presence. I do not think the authors ever estimated specific pixel probabilities or importances. I think they just used statistical distributions. To learn more, I would find reference [11], which they cite as a source for their equation 8.

I am concerned about a possible error in their equation 7. If I could talk to the authors I would ask them if it is possible that the greater than and less than conditions are reversed from where they should be.

The image above is an edited screenshot from the PDF posted here. In equation 7,

=1 if there is a break in presence for user i at time t, and

=0 if there is NOT a break in presence.

is the logical "or" (denoted by the "v" symbol, in blue circle) of two factors. The first factor relates to delay. The second factor relates to video quality. If the delay is too large, or if the quality is too low, or both, there is a break in presence. The ≥ symbol and the ≤ symbol (circled in red) are reversed from what I would expect. Perhaps I do not understand equation 7 correctly.

Equation 7 is used to specify one of hte terms in equaiton 8. Equation 8 is used in equation 9. Equation 9 is the quantity to be minimized by a decentralized learning algorithm.

[11] J. Chung, H. J. Yoon, and H. J. Gardner, “Analysis of break in presence during game play using a linear mixed model,” ETRI journal, vol. 32, no. 5, pp. 687–694, Oct. 2010.

Hudson Romualdo 2022-11-29

编辑：Hudson Romualdo 2022-11-29

Hi @William Rose

I'm trying to implement de (7) equation (same author - and very simillar of that one you used) included in this paper: "Federated Echo State Learning for Minimizing Breaks in Presence in Wireless Virtual Reality Networks"

Your concerne can be atested there: "greater than and less than conditions are reversed from where they should be"

My initial post it's all about Mi,t variable.

How author created the Mi,t vector of ones and zeros representing pixels that are or aren't important to render a frame from a VR Video?

I checked the [11] reference that you suggested once (in my article it's [31]) but couldn't do the relation between the statistics and Mi,t and Li,t vectors. I'll read again.

Obs: same doubt about Li,t vector - but I'm trying to solve one problem at a time. =)

William Rose 2022-11-30

@Hudson Romualdo,

Please email me to continue our discussion. Click on the WR circle next to my name. A window will appear, with an envelope icon in the top right corner. Click on the envelope to send me your email address, and I will reply.

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