How much more magnification does a K-3 gives you-real world

photoptimist · 02-27-2017, 06:53 PM

Originally posted by normhead

But they have become much more interesting with digital...
For example theoretically, if you have a 4912 pixels high image. Obviously if you could make 4912 single pixel lines alternating black and white, you'd have you'd have 4912 distinct lines... so why can you only produce 3500 on a test chart with a K-1, the possibilities for exploration are endless. With analogue, I never asked that question. It just was what it was.

What does 3500 have to do with 4912 ?

Where do the extra 1412 pixel rows go?

It's like the meaning of life being 42.

It's more defined as a question in digital, but, it is still better not to think about it.

Where do the extra 1412 pixel rows go? Losses include the lens, the Bayer filter (a K-1 could only resolve 2456 red-and-black lines), the anti-alias filter (if present or simulated), scattering by the sensor microlenses, and even a bit of cross-pixel photon tunneling.

The interesting issue with DR is in the trade-off between image noise and resolution of those shadow objects. Even if the optics can put a sharp image on the sensor, a tiny feature in the darkness may be indistinguishable from noise if the DR of the pixels is too low or the ISO has been pushed. But a larger feature in the shadows might distinguishable in that noise by downsampling the image. That is, one can turn a noisy image made with 1 µm pixels into a higher quality image as if there were 5 µm pixels by bucketing and averaging.

The other interesting issue is the subtle role of pixel well depth in picture quality and the ability to "print large" even in low-DR images. Resolving a subtle variation in color or intensity (e.g., the alternating pattern of barbs and barbules in a feather) depends on the sensor's ability to resolve small differences in light level. It's not a DR issue because it happens even in bright pixels and would still occur even if one had a magic sensor with zero noise. The issue caused by the statistical properties of arriving photons and the chances that, for example, only 9900 or fewer photons hit the pixel when the average light intensity meant that 10,000 photons should have hit the pixel. That statistical property is a function of well depth at base ISO and any ISO-related amplification of the signal. A tiny-pixel sensor will have more problems resolving high-resolution features that have little variation in intensity. The feather will look more like a patch of color than a distinct but subtle ribbed alternation of color. Again, downsampling can help.

normhead · 02-27-2017, 08:04 PM

Originally posted by photoptimist

Where do the extra 1412 pixel rows go? Losses include the lens, the Bayer filter (a K-1 could only resolve 2456 red-and-black lines), the anti-alias filter (if present or simulated), scattering by the sensor microlenses, and even a bit of cross-pixel photon tunneling.

The interesting issue with DR is in the trade-off between image noise and resolution of those shadow objects. Even if the optics can put a sharp image on the sensor, a tiny feature in the darkness may be indistinguishable from noise if the DR of the pixels is too low or the ISO has been pushed. But a larger feature in the shadows might distinguishable in that noise by downsampling the image. That is, one can turn a noisy image made with 1 µm pixels into a higher quality image as if there were 5 µm pixels by bucketing and averaging.

The other interesting issue is the subtle role of pixel well depth in picture quality and the ability to "print large" even in low-DR images. Resolving a subtle variation in color or intensity (e.g., the alternating pattern of barbs and barbules in a feather) depends on the sensor's ability to resolve small differences in light level. It's not a DR issue because it happens even in bright pixels and would still occur even if one had a magic sensor with zero noise. The issue caused by the statistical properties of arriving photons and the chances that, for example, only 9900 or fewer photons hit the pixel when the average light intensity meant that 10,000 photons should have hit the pixel. That statistical property is a function of well depth at base ISO and any ISO-related amplification of the signal. A tiny-pixel sensor will have more problems resolving high-resolution features that have little variation in intensity. The feather will look more like a patch of color than a distinct but subtle ribbed alternation of color. Again, downsampling can help.

Quote:

That is, one can turn a noisy image made with 1 µm pixels into a higher quality image as if there were 5 µm pixels by bucketing and averaging.

But you just reduced your image to 20% of its former size. In doing so you could easily cost yourself more resolution than you would have lost by using noise reduction software..

Quote:

A tiny-pixel sensor will have more problems resolving high-resolution features that have little variation in intensity.

Ya, they are known for that. But as long as you're shooting 12 bit raw, your colour depth should be good. The simple fact is, in day to day use, phones, point and shoots, many small sensor cameras do just fine. I'm not sure what "having more problems" entails in the real world.

Check out These Are 20 Of The World's Best Photos Taken With Cell Phones | The Huffington Post

They may have had problems but they weren't insurmountable. Meanwhile check out the DOF in those image. If you want shots like that with a 645, you may have more problems. Not just the weight etc. but the narrow DoF thing. Taken as a whole, you could do a whole series on which camera system has more problems. It's not a one way street.

Ian Stuart Forsyth · 02-27-2017, 08:53 PM

Originally posted by photoptimist

I would certainly agree that size in pixels is a decent first approximation to photograph size as far as cropping, enlarging, and printing are concerned. But the approximation seems to fall apart at the extremes of format differences.

Can one honestly say that a 12 MPix crop from a K-1 or 645Z really is the same "size" as a 12 MPix image from an iPhone?

An interesting comparison would be to scale the K1 image by a factor of 1.26 so that they would be compared at the same viewing size, then one could better see what the difference is between the two images. What I have found is that above iso 400 the pixel density of the k3 starts to become less apparent.

normhead · 02-28-2017, 11:24 AM

Originally posted by Ian Stuart Forsyth

An interesting comparison would be to scale the K1 image by a factor of 1.26 so that they would be compared at the same viewing size, then one could better see what the difference is between the two images. What I have found is that above iso 400 the pixel density of the k3 starts to become less apparent.

You could be right. I hate going above 640 ISO using my K-3. After 640, results become unreliable. Even at 800 ISO you can have major noise issues in the right circumstances. Maybe not every time, but often enough to say, if you really want the picture, don't go there.

Part of the key to success with any camera is understanding when not to use it.

02-27-2017, 08:04 PM	#47
normhead Pentaxian Join Date: Jun 2007 Location: Near Algonquin Park Photos: Gallery \| Albums Posts: 40,451 Original Poster	Originally posted by photoptimist Where do the extra 1412 pixel rows go? Losses include the lens, the Bayer filter (a K-1 could only resolve 2456 red-and-black lines), the anti-alias filter (if present or simulated), scattering by the sensor microlenses, and even a bit of cross-pixel photon tunneling. The interesting issue with DR is in the trade-off between image noise and resolution of those shadow objects. Even if the optics can put a sharp image on the sensor, a tiny feature in the darkness may be indistinguishable from noise if the DR of the pixels is too low or the ISO has been pushed. But a larger feature in the shadows might distinguishable in that noise by downsampling the image. That is, one can turn a noisy image made with 1 µm pixels into a higher quality image as if there were 5 µm pixels by bucketing and averaging. The other interesting issue is the subtle role of pixel well depth in picture quality and the ability to "print large" even in low-DR images. Resolving a subtle variation in color or intensity (e.g., the alternating pattern of barbs and barbules in a feather) depends on the sensor's ability to resolve small differences in light level. It's not a DR issue because it happens even in bright pixels and would still occur even if one had a magic sensor with zero noise. The issue caused by the statistical properties of arriving photons and the chances that, for example, only 9900 or fewer photons hit the pixel when the average light intensity meant that 10,000 photons should have hit the pixel. That statistical property is a function of well depth at base ISO and any ISO-related amplification of the signal. A tiny-pixel sensor will have more problems resolving high-resolution features that have little variation in intensity. The feather will look more like a patch of color than a distinct but subtle ribbed alternation of color. Again, downsampling can help. Quote: That is, one can turn a noisy image made with 1 µm pixels into a higher quality image as if there were 5 µm pixels by bucketing and averaging. But you just reduced your image to 20% of its former size. In doing so you could easily cost yourself more resolution than you would have lost by using noise reduction software.. Quote: A tiny-pixel sensor will have more problems resolving high-resolution features that have little variation in intensity. Ya, they are known for that. But as long as you're shooting 12 bit raw, your colour depth should be good. The simple fact is, in day to day use, phones, point and shoots, many small sensor cameras do just fine. I'm not sure what "having more problems" entails in the real world. Check out These Are 20 Of The World's Best Photos Taken With Cell Phones \| The Huffington Post They may have had problems but they weren't insurmountable. Meanwhile check out the DOF in those image. If you want shots like that with a 645, you may have more problems. Not just the weight etc. but the narrow DoF thing. Taken as a whole, you could do a whole series on which camera system has more problems. It's not a one way street. Last edited by normhead; 02-28-2017 at 11:30 AM.

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02-27-2017, 06:53 PM	#46
photoptimist Pentaxian Join Date: Jul 2016 Photos: Albums Posts: 5,127	Originally posted by normhead But they have become much more interesting with digital... For example theoretically, if you have a 4912 pixels high image. Obviously if you could make 4912 single pixel lines alternating black and white, you'd have you'd have 4912 distinct lines... so why can you only produce 3500 on a test chart with a K-1, the possibilities for exploration are endless. With analogue, I never asked that question. It just was what it was. What does 3500 have to do with 4912 ? Where do the extra 1412 pixel rows go? It's like the meaning of life being 42. It's more defined as a question in digital, but, it is still better not to think about it. Where do the extra 1412 pixel rows go? Losses include the lens, the Bayer filter (a K-1 could only resolve 2456 red-and-black lines), the anti-alias filter (if present or simulated), scattering by the sensor microlenses, and even a bit of cross-pixel photon tunneling. The interesting issue with DR is in the trade-off between image noise and resolution of those shadow objects. Even if the optics can put a sharp image on the sensor, a tiny feature in the darkness may be indistinguishable from noise if the DR of the pixels is too low or the ISO has been pushed. But a larger feature in the shadows might distinguishable in that noise by downsampling the image. That is, one can turn a noisy image made with 1 µm pixels into a higher quality image as if there were 5 µm pixels by bucketing and averaging. The other interesting issue is the subtle role of pixel well depth in picture quality and the ability to "print large" even in low-DR images. Resolving a subtle variation in color or intensity (e.g., the alternating pattern of barbs and barbules in a feather) depends on the sensor's ability to resolve small differences in light level. It's not a DR issue because it happens even in bright pixels and would still occur even if one had a magic sensor with zero noise. The issue caused by the statistical properties of arriving photons and the chances that, for example, only 9900 or fewer photons hit the pixel when the average light intensity meant that 10,000 photons should have hit the pixel. That statistical property is a function of well depth at base ISO and any ISO-related amplification of the signal. A tiny-pixel sensor will have more problems resolving high-resolution features that have little variation in intensity. The feather will look more like a patch of color than a distinct but subtle ribbed alternation of color. Again, downsampling can help.

02-27-2017, 08:53 PM	#48
Ian Stuart Forsyth Pentaxian Join Date: May 2009 Location: Somewhere over the rainbow Photos: Gallery Posts: 1,531	Originally posted by photoptimist I would certainly agree that size in pixels is a decent first approximation to photograph size as far as cropping, enlarging, and printing are concerned. But the approximation seems to fall apart at the extremes of format differences. Can one honestly say that a 12 MPix crop from a K-1 or 645Z really is the same "size" as a 12 MPix image from an iPhone? An interesting comparison would be to scale the K1 image by a factor of 1.26 so that they would be compared at the same viewing size, then one could better see what the difference is between the two images. What I have found is that above iso 400 the pixel density of the k3 starts to become less apparent.

02-28-2017, 11:24 AM	#49
normhead Pentaxian Join Date: Jun 2007 Location: Near Algonquin Park Photos: Gallery \| Albums Posts: 40,451 Original Poster	Originally posted by Ian Stuart Forsyth An interesting comparison would be to scale the K1 image by a factor of 1.26 so that they would be compared at the same viewing size, then one could better see what the difference is between the two images. What I have found is that above iso 400 the pixel density of the k3 starts to become less apparent. You could be right. I hate going above 640 ISO using my K-3. After 640, results become unreliable. Even at 800 ISO you can have major noise issues in the right circumstances. Maybe not every time, but often enough to say, if you really want the picture, don't go there. Part of the key to success with any camera is understanding when not to use it.