Shot Noise SNR formula is no use to my eyes.

Great Bustard said:

mostlyboringphotog said:

Great Bustard said:

mostlyboringphotog said:

davidli said:

Thanks for your replies. I believe that we see the key point that when we talk about observing with our eyes, we have to compare the SNR on a display or print out.

Assume that only "shot noise" is the dominant noise.

SNR of output dispaly or image from Sensor A / SNR of output display or image from Sensor B
= sqrt(Ra / Rb) * (SNRa / SNRb) where Ra is Pixel count of Sensor A and Rb ...
= ..... (because SNR = sqrt(Signal) ∝ sqrt(Photosite Area)
= sqrt(Area of whole sensor A / Area of whole Sensor B)
= 1

Click to expand...

Some folks believe SNR is baked in when the image is made and does not change with display/print size. I happened to think that it does change.

Click to expand...

Does the resolution change?

Click to expand...

I would think so...

Click to expand...

Interesting. How does that happen?

Click to expand...

I just took a photograph in the sunshine on a lovely Chinook warm day of a half-naked lady After a very likable image is resize with bicubic automatic (in Photoshop) the following is noticed:

pixel dimension, linear dimension reduction, image Std Deviation

3840x5760, 100%, 64.47

1920x2880, 50%, 64.54

960x1440, 25%, 64.77

Resizing the 25% image using bicubic automatic back to full resolution 100% I get:

3840x5760, 100% 64.69

From this I infer that, since the total image standard deviation is virtually unchanged in any of the up or down resolution operations (i.e. in MBP's terms, "change the display/print size"), the signal to noise ratio is invariant with up/down scale. Of course, I assume that any mathematical operation on an image must introduce at least a slight bit of noise, but it is inconsequential, an integer mathematical oddity and does exist.

After each resize, with an obvious change of image resolution detail, you have a unique new image with it's own SNR and standard deviation, but they are so close in terms of noise as to be "the same".

Is this a valid interpretation ??

--
Charles Darwin: "ignorance more frequently begets confidence than does knowledge."
tony
http://www.tphoto.ca

tony field said:

Great Bustard said:

mostlyboringphotog said:

Great Bustard said:

mostlyboringphotog said:

davidli said:

Thanks for your replies. I believe that we see the key point that when we talk about observing with our eyes, we have to compare the SNR on a display or print out.

Assume that only "shot noise" is the dominant noise.

SNR of output dispaly or image from Sensor A / SNR of output display or image from Sensor B
= sqrt(Ra / Rb) * (SNRa / SNRb) where Ra is Pixel count of Sensor A and Rb ...
= ..... (because SNR = sqrt(Signal) ∝ sqrt(Photosite Area)
= sqrt(Area of whole sensor A / Area of whole Sensor B)
= 1

Click to expand...

Some folks believe SNR is baked in when the image is made and does not change with display/print size. I happened to think that it does change.

Click to expand...

Does the resolution change?

Click to expand...

I would think so...

Click to expand...

Interesting. How does that happen?

Click to expand...

I just took a photograph in the sunshine on a lovely Chinook warm day of a half-naked lady After a very likable image is resize with bicubic automatic (in Photoshop) the following is noticed:

pixel dimension, linear dimension reduction, image Std Deviation

3840x5760, 100%, 64.47

1920x2880, 50%, 64.54

960x1440, 25%, 64.77

Resizing the 25% image using bicubic automatic back to full resolution 100% I get:

3840x5760, 100% 64.69

From this I infer that, since the total image standard deviation is virtually unchanged in any of the up or down resolution operations (i.e. in MBP's terms, "change the display/print size"), the signal to noise ratio is invariant with up/down scale. Of course, I assume that any mathematical operation on an image must introduce at least a slight bit of noise, but it is inconsequential, an integer mathematical oddity and does exist.

After each resize, with an obvious change of image resolution detail, you have a unique new image with it's own SNR and standard deviation, but they are so close in terms of noise as to be "the same".

Is this a valid interpretation ??

Click to expand...

I'm sorry -- what were you saying? My mind kinda wandered after the "half-naked lady" part. :-D

Great Bustard said:

tony field said:

Great Bustard said:

mostlyboringphotog said:

Great Bustard said:

mostlyboringphotog said:

davidli said:

Thanks for your replies. I believe that we see the key point that when we talk about observing with our eyes, we have to compare the SNR on a display or print out.

Assume that only "shot noise" is the dominant noise.

SNR of output dispaly or image from Sensor A / SNR of output display or image from Sensor B
= sqrt(Ra / Rb) * (SNRa / SNRb) where Ra is Pixel count of Sensor A and Rb ...
= ..... (because SNR = sqrt(Signal) ∝ sqrt(Photosite Area)
= sqrt(Area of whole sensor A / Area of whole Sensor B)
= 1

Click to expand...

Some folks believe SNR is baked in when the image is made and does not change with display/print size. I happened to think that it does change.

Click to expand...

Does the resolution change?

Click to expand...

I would think so...

Click to expand...

Interesting. How does that happen?

Click to expand...

I just took a photograph in the sunshine on a lovely Chinook warm day of a half-naked lady After a very likable image is resize with bicubic automatic (in Photoshop) the following is noticed:

pixel dimension, linear dimension reduction, image Std Deviation

3840x5760, 100%, 64.47

1920x2880, 50%, 64.54

960x1440, 25%, 64.77

Resizing the 25% image using bicubic automatic back to full resolution 100% I get:

3840x5760, 100% 64.69

From this I infer that, since the total image standard deviation is virtually unchanged in any of the up or down resolution operations (i.e. in MBP's terms, "change the display/print size"), the signal to noise ratio is invariant with up/down scale. Of course, I assume that any mathematical operation on an image must introduce at least a slight bit of noise, but it is inconsequential, an integer mathematical oddity and does exist.

After each resize, with an obvious change of image resolution detail, you have a unique new image with it's own SNR and standard deviation, but they are so close in terms of noise as to be "the same".

Is this a valid interpretation ??

Click to expand...

I'm sorry -- what were you saying? My mind kinda wandered after the "half-naked lady" part. :-D

Click to expand...

It is a raw image

None the less, the question is serious !

Great Bustard said:

mostlyboringphotog said:

Great Bustard said:

mostlyboringphotog said:

davidli said:

Thanks for your replies. I believe that we see the key point that when we talk about observing with our eyes, we have to compare the SNR on a display or print out.

Assume that only "shot noise" is the dominant noise.

SNR of output dispaly or image from Sensor A / SNR of output display or image from Sensor B
= sqrt(Ra / Rb) * (SNRa / SNRb) where Ra is Pixel count of Sensor A and Rb ...
= ..... (because SNR = sqrt(Signal) ∝ sqrt(Photosite Area)
= sqrt(Area of whole sensor A / Area of whole Sensor B)
= 1

Click to expand...

Some folks believe SNR is baked in when the image is made and does not change with display/print size. I happened to think that it does change.

Click to expand...

Does the resolution change?

Click to expand...

I would think so...

Click to expand...

Interesting. How does that happen?

Click to expand...

So, it was a trick question and, blast it, I fell for it again

So same FOV RGB image, same size of 4x6, one with 4000x60000 dimension and one with 400x600 dimension, which has more resolution?

So same FOV RGB image, same size of 4x6, one with 4000x60000 dimension and one with 400x600 dimension, which looks sharper?

So same FOV RGB image, same size of 4x6, one with 4000x60000 dimension and one with 400x600 dimension, which looks more contrasty?

tony field said:

Great Bustard said:

tony field said:

Great Bustard said:

mostlyboringphotog said:

Great Bustard said:

mostlyboringphotog said:

davidli said:

Thanks for your replies. I believe that we see the key point that when we talk about observing with our eyes, we have to compare the SNR on a display or print out.

Assume that only "shot noise" is the dominant noise.

SNR of output dispaly or image from Sensor A / SNR of output display or image from Sensor B
= sqrt(Ra / Rb) * (SNRa / SNRb) where Ra is Pixel count of Sensor A and Rb ...
= ..... (because SNR = sqrt(Signal) ∝ sqrt(Photosite Area)
= sqrt(Area of whole sensor A / Area of whole Sensor B)
= 1

Click to expand...

Some folks believe SNR is baked in when the image is made and does not change with display/print size. I happened to think that it does change.

Click to expand...

Does the resolution change?

Click to expand...

I would think so...

Click to expand...

Interesting. How does that happen?

Click to expand...

I just took a photograph in the sunshine on a lovely Chinook warm day of a half-naked lady After a very likable image is resize with bicubic automatic (in Photoshop) the following is noticed:

pixel dimension, linear dimension reduction, image Std Deviation

3840x5760, 100%, 64.47

1920x2880, 50%, 64.54

960x1440, 25%, 64.77

Resizing the 25% image using bicubic automatic back to full resolution 100% I get:

3840x5760, 100% 64.69

From this I infer that, since the total image standard deviation is virtually unchanged in any of the up or down resolution operations (i.e. in MBP's terms, "change the display/print size"), the signal to noise ratio is invariant with up/down scale. Of course, I assume that any mathematical operation on an image must introduce at least a slight bit of noise, but it is inconsequential, an integer mathematical oddity and does exist.

After each resize, with an obvious change of image resolution detail, you have a unique new image with it's own SNR and standard deviation, but they are so close in terms of noise as to be "the same".

Is this a valid interpretation ??

Click to expand...

I'm sorry -- what were you saying? My mind kinda wandered after the "half-naked lady" part. :-D

Click to expand...

Click to expand...

It could be the half-naked part as opposed to just a naked lady...

tony field said:

It is a raw image

None the less, the question is serious !

--
Charles Darwin: "ignorance more frequently begets confidence than does knowledge."
tony
http://www.tphoto.ca

Click to expand...

... "hoodood the hoodoo man".

mostlyboringphotog said:

Great Bustard said:

mostlyboringphotog said:

Great Bustard said:

mostlyboringphotog said:

Some folks believe SNR is baked in when the image is made and does not change with display/print size. I happened to think that it does change.

Click to expand...

Does the resolution change?

Click to expand...

I would think so...

Click to expand...

Interesting. How does that happen?

Click to expand...

So, it was a trick question and, blast it, I fell for it again

Click to expand...

No, it was a question intended to test your understanding of (spatially normalized) displayed SNRs of recorded/processed (uniform-field) images. Edhannon seems to be the resident expert on such things.

mostlyboringphotog said:

Great Bustard said:

mostlyboringphotog said:

Great Bustard said:

mostlyboringphotog said:

davidli said:

Thanks for your replies. I believe that we see the key point that when we talk about observing with our eyes, we have to compare the SNR on a display or print out.

Assume that only "shot noise" is the dominant noise.

SNR of output dispaly or image from Sensor A / SNR of output display or image from Sensor B
= sqrt(Ra / Rb) * (SNRa / SNRb) where Ra is Pixel count of Sensor A and Rb ...
= ..... (because SNR = sqrt(Signal) ∝ sqrt(Photosite Area)
= sqrt(Area of whole sensor A / Area of whole Sensor B)
= 1

Click to expand...

Some folks believe SNR is baked in when the image is made and does not change with display/print size. I happened to think that it does change.

Click to expand...

Does the resolution change?

Click to expand...

I would think so...

Click to expand...

Interesting. How does that happen?

Click to expand...

So, it was a trick question and, blast it, I fell for it again

Click to expand...

No trick. Just a question not unlike what the cops ask me all the time, "Do you know what the speed limit here is?" ;-)

mostlyboringphotog said:

So same FOV RGB image, same size of 4x6, one with 4000x60000 dimension and one with 400x600 dimension, which has more resolution?

Click to expand...

Dunno. Let's say both were recorded on a 4000x6000 sensor with the same lens and settings. Then the resolutions would be the same.

mostlyboringphotog said:

So same FOV RGB image, same size of 4x6, one with 4000x60000 dimension and one with 400x600 dimension, which looks sharper?

Click to expand...

Sharpness is a whole other conversation.

mostlyboringphotog said:

So same FOV RGB image, same size of 4x6, one with 4000x60000 dimension and one with 400x600 dimension, which looks more contrasty?

Click to expand...

Same as above. Let's stick with resolution for the time being. How does the display size affect the resolution in the photo?

tony field said:

Great Bustard said:

tony field said:

Great Bustard said:

mostlyboringphotog said:

Great Bustard said:

mostlyboringphotog said:

davidli said:

Thanks for your replies. I believe that we see the key point that when we talk about observing with our eyes, we have to compare the SNR on a display or print out.

Assume that only "shot noise" is the dominant noise.

SNR of output dispaly or image from Sensor A / SNR of output display or image from Sensor B
= sqrt(Ra / Rb) * (SNRa / SNRb) where Ra is Pixel count of Sensor A and Rb ...
= ..... (because SNR = sqrt(Signal) ∝ sqrt(Photosite Area)
= sqrt(Area of whole sensor A / Area of whole Sensor B)
= 1

Click to expand...

Some folks believe SNR is baked in when the image is made and does not change with display/print size. I happened to think that it does change.

Click to expand...

Does the resolution change?

Click to expand...

I would think so...

Click to expand...

Interesting. How does that happen?

Click to expand...

I just took a photograph in the sunshine on a lovely Chinook warm day of a half-naked lady After a very likable image is resize with bicubic automatic (in Photoshop) the following is noticed:

pixel dimension, linear dimension reduction, image Std Deviation

3840x5760, 100%, 64.47

1920x2880, 50%, 64.54

960x1440, 25%, 64.77

Resizing the 25% image using bicubic automatic back to full resolution 100% I get:

3840x5760, 100% 64.69

From this I infer that, since the total image standard deviation is virtually unchanged in any of the up or down resolution operations (i.e. in MBP's terms, "change the display/print size"), the signal to noise ratio is invariant with up/down scale. Of course, I assume that any mathematical operation on an image must introduce at least a slight bit of noise, but it is inconsequential, an integer mathematical oddity and does exist.

After each resize, with an obvious change of image resolution detail, you have a unique new image with it's own SNR and standard deviation, but they are so close in terms of noise as to be "the same".

Is this a valid interpretation ??

Click to expand...

I'm sorry -- what were you saying? My mind kinda wandered after the "half-naked lady" part. :-D

Click to expand...

It is a raw image

None the less, the question is serious !

Click to expand...

To answer the question seriously, I would say that downsampling merely cuts off higher frequencies of noise but, aside from that, doesn't affect noise at the remaining frequencies.

Great Bustard said:

tony field said:

Great Bustard said:

tony field said:

Great Bustard said:

mostlyboringphotog said:

Great Bustard said:

mostlyboringphotog said:

davidli said:

Thanks for your replies. I believe that we see the key point that when we talk about observing with our eyes, we have to compare the SNR on a display or print out.

Assume that only "shot noise" is the dominant noise.

SNR of output dispaly or image from Sensor A / SNR of output display or image from Sensor B
= sqrt(Ra / Rb) * (SNRa / SNRb) where Ra is Pixel count of Sensor A and Rb ...
= ..... (because SNR = sqrt(Signal) ∝ sqrt(Photosite Area)
= sqrt(Area of whole sensor A / Area of whole Sensor B)
= 1

Click to expand...

Some folks believe SNR is baked in when the image is made and does not change with display/print size. I happened to think that it does change.

Click to expand...

Does the resolution change?

Click to expand...

I would think so...

Click to expand...

Interesting. How does that happen?

Click to expand...

I just took a photograph in the sunshine on a lovely Chinook warm day of a half-naked lady After a very likable image is resize with bicubic automatic (in Photoshop) the following is noticed:

pixel dimension, linear dimension reduction, image Std Deviation

3840x5760, 100%, 64.47

1920x2880, 50%, 64.54

960x1440, 25%, 64.77

Resizing the 25% image using bicubic automatic back to full resolution 100% I get:

3840x5760, 100% 64.69

From this I infer that, since the total image standard deviation is virtually unchanged in any of the up or down resolution operations (i.e. in MBP's terms, "change the display/print size"), the signal to noise ratio is invariant with up/down scale. Of course, I assume that any mathematical operation on an image must introduce at least a slight bit of noise, but it is inconsequential, an integer mathematical oddity and does exist.

After each resize, with an obvious change of image resolution detail, you have a unique new image with it's own SNR and standard deviation, but they are so close in terms of noise as to be "the same".

Is this a valid interpretation ??

Click to expand...

I'm sorry -- what were you saying? My mind kinda wandered after the "half-naked lady" part. :-D

Click to expand...

It is a raw image

None the less, the question is serious !

Click to expand...

To answer the question seriously, I would say that downsampling merely cuts off higher frequencies of noise but, aside from that, doesn't affect noise at the remaining frequencies.

Click to expand...

That makes sense. With that I infer that the noise distribution changes for a "local part of the image", in particular where there is high frequency detail and associated (probably high frequency photon) noise: however the resulting overall image SNR does not change (assuming that the standard deviation reported by Photoshop is a reflection of image AND SNR).

Of course, normally I use the Photoshop Std Dev when "evaluating" very small similar/identical areas of images (e.g. at different ISO values among cameras) . I was just trying to understand the implications for a full image. For a tiny section (say 150x150 pixels) of an ISO 1600 image with it's resulting SNR, the Std Dev also seems invariant when the image is down-sized to 25% of the 150x150 square.

--
Charles Darwin: "ignorance more frequently begets confidence than does knowledge."
tony
http://www.tphoto.ca

tony field said:

tony field said:

tony field said:

tony field said:

tony field said:

I just took a photograph in the sunshine on a lovely Chinook warm day of a half-naked lady After a very likable image is resize with bicubic automatic (in Photoshop) the following is noticed:

pixel dimension, linear dimension reduction, image Std Deviation

3840x5760, 100%, 64.47

1920x2880, 50%, 64.54

960x1440, 25%, 64.77

Resizing the 25% image using bicubic automatic back to full resolution 100% I get:

3840x5760, 100% 64.69

From this I infer that, since the total image standard deviation is virtually unchanged in any of the up or down resolution operations (i.e. in MBP's terms, "change the display/print size"), the signal to noise ratio is invariant with up/down scale. Of course, I assume that any mathematical operation on an image must introduce at least a slight bit of noise, but it is inconsequential, an integer mathematical oddity and does exist.

After each resize, with an obvious change of image resolution detail, you have a unique new image with it's own SNR and standard deviation, but they are so close in terms of noise as to be "the same".

Is this a valid interpretation ??

Click to expand...

Click to expand...

Click to expand...

Click to expand...

That makes sense. With that I infer that the noise distribution changes for a "local part of the image", in particular where there is high frequency detail and associated (probably high frequency photon) noise: however the resulting overall image SNR does not change (assuming that the standard deviation reported by Photoshop is a reflection of image AND SNR).

Click to expand...

The standard deviation calculated over the entire image does not tell us much about noise, unless the image happens to be of a perfectly uniform target (and no significant vignetting, etc. is present). For the image of said lady, the standard deviation over the entire image will respond more strongly to contrast, rather than noise.

tony field said:

Of course, normally I use the Photoshop Std Dev when "evaluating" very small similar/identical areas of images (e.g. at different ISO values among cameras) . I was just trying to understand the implications for a full image. For a tiny section (say 150x150 pixels) of an ISO 1600 image with it's resulting SNR, the Std Dev also seems invariant when the image is down-sized to 25% of the 150x150 square.

Click to expand...

This could be more of a reflection on Photoshop's bicubic resampling algorithm than the response of SNR (in a uniform image patch) to resampling. I have seen some significant overshoot with popular bicubic resampling filters (e.g. Mitchell). This overshoot will tend to increase the apparent contrast of higher frequencies, i.e., amplify noise, thus countering the increase in SNR resulting from the averaging effects of the resampling filter.

Although I am pretty sure someone will now reply to my post saying that binning (or unweighted averaging over an equal-sized physical sensor area, comparing images/sensors of different resolution) is an inferior resampling method, the point is that shot noise will behave as expected when performing unweighted averaging.

For most actual photgraphic purposes, cubic/lanczos/sinc resampling would be preferable, but my point is that we should choose the correct resampling approach depending on what we are trying to accomplish.

fvdbergh2501 said:

tony field said:

fvdbergh2501 said:

fvdbergh2501 said:

fvdbergh2501 said:

fvdbergh2501 said:

I just took a photograph in the sunshine on a lovely Chinook warm day of a half-naked lady After a very likable image is resize with bicubic automatic (in Photoshop) the following is noticed:

pixel dimension, linear dimension reduction, image Std Deviation

3840x5760, 100%, 64.47

1920x2880, 50%, 64.54

960x1440, 25%, 64.77

Resizing the 25% image using bicubic automatic back to full resolution 100% I get:

3840x5760, 100% 64.69

From this I infer that, since the total image standard deviation is virtually unchanged in any of the up or down resolution operations (i.e. in MBP's terms, "change the display/print size"), the signal to noise ratio is invariant with up/down scale. Of course, I assume that any mathematical operation on an image must introduce at least a slight bit of noise, but it is inconsequential, an integer mathematical oddity and does exist.

After each resize, with an obvious change of image resolution detail, you have a unique new image with it's own SNR and standard deviation, but they are so close in terms of noise as to be "the same".

Is this a valid interpretation ??

Click to expand...

Click to expand...

Click to expand...

Click to expand...

That makes sense. With that I infer that the noise distribution changes for a "local part of the image", in particular where there is high frequency detail and associated (probably high frequency photon) noise: however the resulting overall image SNR does not change (assuming that the standard deviation reported by Photoshop is a reflection of image AND SNR).

Click to expand...

The standard deviation calculated over the entire image does not tell us much about noise, unless the image happens to be of a perfectly uniform target (and no significant vignetting, etc. is present). For the image of said lady, the standard deviation over the entire image will respond more strongly to contrast, rather than noise.

fvdbergh2501 said:

Of course, normally I use the Photoshop Std Dev when "evaluating" very small similar/identical areas of images (e.g. at different ISO values among cameras) . I was just trying to understand the implications for a full image. For a tiny section (say 150x150 pixels) of an ISO 1600 image with it's resulting SNR, the Std Dev also seems invariant when the image is down-sized to 25% of the 150x150 square.

Click to expand...

This could be more of a reflection on Photoshop's bicubic resampling algorithm than the response of SNR (in a uniform image patch) to resampling. I have seen some significant overshoot with popular bicubic resampling filters (e.g. Mitchell). This overshoot will tend to increase the apparent contrast of higher frequencies, i.e., amplify noise, thus countering the increase in SNR resulting from the averaging effects of the resampling filter.

Although I am pretty sure someone will now reply to my post saying that binning (or unweighted averaging over an equal-sized physical sensor area, comparing images/sensors of different resolution) is an inferior resampling method, the point is that shot noise will behave as expected when performing unweighted averaging.

For most actual photgraphic purposes, cubic/lanczos/sinc resampling would be preferable, but my point is that we should choose the correct resampling approach depending on what we are trying to accomplish.

Click to expand...

Thanks for the clear description. Looks like I have more homework in this area.

fvdbergh2501 said:

tony field said:

fvdbergh2501 said:

fvdbergh2501 said:

fvdbergh2501 said:

fvdbergh2501 said:

I just took a photograph in the sunshine on a lovely Chinook warm day of a half-naked lady After a very likable image is resize with bicubic automatic (in Photoshop) the following is noticed:

pixel dimension, linear dimension reduction, image Std Deviation

3840x5760, 100%, 64.47

1920x2880, 50%, 64.54

960x1440, 25%, 64.77

Resizing the 25% image using bicubic automatic back to full resolution 100% I get:

3840x5760, 100% 64.69

From this I infer that, since the total image standard deviation is virtually unchanged in any of the up or down resolution operations (i.e. in MBP's terms, "change the display/print size"), the signal to noise ratio is invariant with up/down scale. Of course, I assume that any mathematical operation on an image must introduce at least a slight bit of noise, but it is inconsequential, an integer mathematical oddity and does exist.

After each resize, with an obvious change of image resolution detail, you have a unique new image with it's own SNR and standard deviation, but they are so close in terms of noise as to be "the same".

Is this a valid interpretation ??

Click to expand...

Click to expand...

Click to expand...

Click to expand...

Click to expand...

Click to expand...

I recommend that you read these (complex, authoritative, and with some graphics) posts by ejmartin:

http://www.dpreview.com/forums/post/30378204

http://www.dpreview.com/forums/post/30381393

http://www.dpreview.com/forums/post/30382813

http://www.dpreview.com/forums/post/30394220

A recent post of mine on the subject (compiling and quoting some cogent statements by ejmartin):

http://www.dpreview.com/forums/post/56810863

.

fvdbergh2501 said:

fvdbergh2501 said:

That makes sense. With that I infer that the noise distribution changes for a "local part of the image", in particular where there is high frequency detail and associated (probably high frequency photon) noise: however the resulting overall image SNR does not change (assuming that the standard deviation reported by Photoshop is a reflection of image AND SNR).

Click to expand...

The standard deviation calculated over the entire image does not tell us much about noise, unless the image happens to be of a perfectly uniform target (and no significant vignetting, etc. is present). For the image of said lady, the standard deviation over the entire image will respond more strongly to contrast, rather than noise.

Click to expand...

Wise words, Frans ! The "Peak Signal/Noise Ratio" metric is a function of the "Mean Squared Error" - and the text of this paper (kindly referred-to and linked-to a while back by Joofa), and some of it's example test-images included within the paper seems to twist and shred any "comforts" surrounding complex images (as opposed to simple "uniform-fields"). No wonder that "image-level SNR" remains elusive ?

See: https://ece.uwaterloo.ca/~z70wang/publications/SPM09.pdf

fvdbergh2501 said:

fvdbergh2501 said:

Of course, normally I use the Photoshop Std Dev when "evaluating" very small similar/identical areas of images (e.g. at different ISO values among cameras) . I was just trying to understand the implications for a full image. For a tiny section (say 150x150 pixels) of an ISO 1600 image with it's resulting SNR, the Std Dev also seems invariant when the image is down-sized to 25% of the 150x150 square.

Click to expand...

Click to expand...

The SNR "invariance" that you (and I) speak of seems to me to only exist in cases where equal-magnitude (desired) "signal" spectrum extends up to (but not higher than) the spatial frequency limits of the imaging-system "front-end" ("corner-frequency-limited", a.k.a. "band-limited", to avoid aliasing).

In case of (so-called) "natural images", down-sampling can have an effect of improving measure-able SNR at higher spatial frequencies (where image-noise existed prior to down-sampling, but limited or possibly no "signal" at the higher spatial frequencies that are down-sampled ever existed in the first place).

Down-sampling performs what is (effectively) a "low-pass spatial frequency filter" effect on: the "noise"; as well as upon any "signal" present. So, "it rather all depends" where it comes to the possibilities for SNR improvement through down-sampling where it comes to processing complex images.

Not to be ignored is the human visual system's spatial frequency response relative to viewing-parameters.

fvdbergh2501 said:

This could be more of a reflection on Photoshop's bicubic resampling algorithm than the response of SNR (in a uniform image patch) to resampling. I have seen some significant overshoot with popular bicubic resampling filters (e.g. Mitchell). This overshoot will tend to increase the apparent contrast of higher frequencies, i.e., amplify noise, thus countering the increase in SNR resulting from the averaging effects of the resampling filter.

Although I am pretty sure someone will now reply to my post saying that binning (or unweighted averaging over an equal-sized physical sensor area, comparing images/sensors of different resolution) is an inferior resampling method, the point is that shot noise will behave as expected when performing unweighted averaging.

For most actual photgraphic purposes, cubic/lanczos/sinc resampling would be preferable, but my point is that we should choose the correct resampling approach depending on what we are trying to accomplish.

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Regarding PS Bicubic re-samplings, this Jason Summers fellow says that he has used his "ResampleScope" (Summers' utility for attempting to discover the re-sampling algorithm that is used on a re-sampled image):

http://entropymine.com/resamplescope/

... to analyze (2011) PS resampling algorithms with coefficients listed here:

http://entropymine.com/resamplescope/notes/photoshop/

... that exist within the (what he says is a "standard") Cubic identity given here:

http://entropymine.com/imageworsener/bicubic

Jason Summers has a freeware command-line re-sampling utility of his very own here:

http://entropymine.com/imageworsener/

.

DM

Great Bustard said:

mostlyboringphotog said:

Great Bustard said:

mostlyboringphotog said:

Great Bustard said:

mostlyboringphotog said:

davidli said:

Thanks for your replies. I believe that we see the key point that when we talk about observing with our eyes, we have to compare the SNR on a display or print out.

Assume that only "shot noise" is the dominant noise.

SNR of output dispaly or image from Sensor A / SNR of output display or image from Sensor B
= sqrt(Ra / Rb) * (SNRa / SNRb) where Ra is Pixel count of Sensor A and Rb ...
= ..... (because SNR = sqrt(Signal) ∝ sqrt(Photosite Area)
= sqrt(Area of whole sensor A / Area of whole Sensor B)
= 1

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Some folks believe SNR is baked in when the image is made and does not change with display/print size. I happened to think that it does change.

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Does the resolution change?

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I would think so...

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Interesting. How does that happen?

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So, it was a trick question and, blast it, I fell for it again

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No trick. Just a question not unlike what the cops ask me all the time, "Do you know what the speed limit here is?" ;-)

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I really do not mind ;-)

Great Bustard said:

Great Bustard said:

So same FOV RGB image, same size of 4x6, one with 4000x60000 dimension and one with 400x600 dimension, which has more resolution?

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Dunno. Let's say both were recorded on a 4000x6000 sensor with the same lens and settings. Then the resolutions would be the same.

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Yes, and then the image was converted to 4000x6000 jpeg and 400x600 jpeg; is the resolution still the same? Do you expect to see same number of line pairs?

Great Bustard said:

Great Bustard said:

So same FOV RGB image, same size of 4x6, one with 4000x60000 dimension and one with 400x600 dimension, which looks sharper?

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Sharpness is a whole other conversation.

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Do you expect the "edge detection" look the same?

Great Bustard said:

Great Bustard said:

So same FOV RGB image, same size of 4x6, one with 4000x60000 dimension and one with 400x600 dimension, which looks more contrasty?

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Same as above.

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Do you expect the black and white level to be the same?

Great Bustard said:

Let's stick with resolution for the time being. How does the display size affect the resolution in the photo?

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OK, let's count the number of line pairs per mm; do you expect lp/mm to be the same?

Detail Man said:

davidli said:

Thanks for your reply. Could you suggest some references to read about? Or some references for the newbie me.

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The .Manual of Photography, Ninth Edition. is a helpful reference in general in several respects regarding the machinery" of photographic imaging-systems - but (even the more recent editions of the book) really do not delve much at all into the kind of subject-matter that you are inquiring about.

.Emil Martinec's web-pages. represent a valued reference-source in several respects. However, as more current image-sensor designs tend more towards "ISO-invariant input-referred Read Noise" (more often referred to as "ISO-less" image-sensor characteristics), certain aspects are changing.

Falk Lumo's web-site. has some interesting information.

.

Various (per-photosite only) level analyses exist on the internet, such as:

http://hamamatsu.magnet.fsu.edu/articles/ccdsnr.html

However, reliable *image-level* analysis is "non-centralized" and more obscure (although I am sure that this author of .this opus. might disagree ... ). I recommend avoiding (or being very cautious regarding the veracity of) alleged information found on (a large majority of) personally published web-pages/blogs - as well as a similar caution when reading various photography forum posts (in general). "Caveat emptor".

.

A few exceptions (to my "jaundiced" outlook regarding individual sources) are these blog-sites of members:

Frans van den Bergh: http://mtfmapper.blogspot.it/

Jack Hogan: http://www.strollswithmydog.com/

Jim Kasson: http://blog.kasson.com/

(Other) DPReview forum contributors that I have learned some valuable informational things from are:

Iliah Borg

bobn2

alanr0

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mosswings

Detail Man said:

The_Suede

DSPographer

Eric Fossum

John Sheehy

Marianne Oelund

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Great Bustard (on those sometimes rare occasions when he manages to "behave himself" ... )

Detail Man said:

.

... and Bob Atkins' well-written articles (linked-to here): http://www.bobatkins.com/photography/

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