4k Tri-Sensor... Epic?

[Pawel Achtel]

At 5K and f/5.6 the improvement is still big. Huge, in fact. Get a 50D (which is almost exactly 5K) and see for yourself.

Diffraction limited doesn't mean there is *no* improvement from more resolution, it just means the improvement will be *smaller* than if it would have been if the aperture weren't diffraction limited. I.e., instead of a 50% improvement in resolution, it's only 40%.

Big? Huge? Is, what 2%?

Diffraction limit is diffraction limit - there is basically insignificant or no improvement in MTF by reducing pixel size below 6 microns when shooting at f/5.6 and beyond. I haven't tried (and don't want to try) 50D, but laws of physics are unavoidable. No point spending $1.5k to attempt to show otherwise.

This is not to say you can't gain sharpness out of 5k+ sensor by using faster and sharper lenses. In fact, when projecting Master Primes, I was nicely suprised they held up 120 lp/mm corner-to-corner open wide at f/1.2 (T1.3).

But then, 120lp/mm is about ~5k anyway, so if not diffraction limit of the lens, it is the limit of other aberrations of the glass that we hit at this resolution.

I'd hate to pull focus with CoC of 4 microns at f/1.2.

[Joseph Ward]

I tried finding if it mentioned if rolling/global shutter and other differ read-reset times, price for tech, ect., that would contribute to the final results? Ohh wait its by Kodak!

[Stephen Williams]

Ohh wait its by Kodak!

Hi,

I use a great deal of film but not much Kodak as I prefer Fuji. I have always found Kodak analysis to be accurate, remember they invented the Bayer chip so have no axe to grind, just trying to get the best out of any technology.

Stephen

[Graeme Nattress]

Daniel, as you know I'm always open to a good discussion!

Sure, if you scale things, the DR of the 2k camera will be the same as the 4k one. However, and I'm not a sensor guru remember, I don't think pixel design quite scales linearly like that. And that's where we'd have to have a guru educate me further.

In the end though, it's total area of sensor used to capture light that really defines the maximal dynamic range of a sensor. What higher resolution does is allow you more flexibility to do some trading.

Graeme

[Dj Joofa]

Another important factor is resampling. Random noise adds in quadrature. So when you resample 4K down to 2K so you can compare dynamic range with the 2K camera, the signal-to-noise ratio doubles, increasing dynamic range by a full stop. (Going 3K to 2K would only increase dynamic range by 1.4X.)

Hi Daniel,

That is just one side of the picture, and not a full analysis. When you do such resampling as you described, then actually the SNR is determined by two factors:

SNR = (SNR_b decrease from blurring) + (SNR_n increase from denoising)

I have mostly seen people talking about the SNR increase from denoising (viz., random noise adds in quadrature if considered uncorrelated, etc.). However, unfortunately, many times the effect of SNR decrease from blurring of data because of the window size is not taken into account.

Since, SNR_b is decreasing and SNR_n is increasing then there must an optimum point where the sum is maximum, for max SNR. Therefore, it becomes an optimization problem to find the appropriate parameters for max SNR.

Given a particular window type (when you added noise in quadrature you actually assumed rectangular window), explicit close form solutions may be derived that relate the optimum resampling parameters.

[Daniel Browning]

Diffraction limit is diffraction limit - there is basically insignificant or no improvement in MTF by reducing pixel size below 6 microns when shooting at f/5.6 and beyond. I haven't tried (and don't want to try) 50D, but laws of physics are unavoidable. No point spending $1.5k to attempt to show otherwise.

I wrote a lengthy response in a new thread, because it seemed a little off topic for this one.

[Daniel Browning]

I'm not a sensor guru either, I just measure RAW files. :-)

I don't think pixel design quite scales linearly like that.

You're right. That was a bad example because real life isn't like that.

The point I should have made was that read noise doesn't have to scale linearly in order for dynamic range to stay the same: it only has to scale with the square root of the decrease in area. That is, change camera B in the above example from "15 electrons" of read noise to "30 electrons" and the 1 stop dynamic range advantage goes away and both cameras become the same. So for every fourfold reduction in size, the read noise per pixel only has to be reduced by half to keep dynamic range the same.

What I've seen is that for every new sensor with smaller pixels, read noise per area (not per pixel) has stayed the same or decreased (sometimes only at high analog gain, but still it's there), with few exceptions. The next question is: why?

Shrinking the pixel is a leap in technology. I think that just the act of miniaturizing the pixel by a factor of four has the natural effect of reducing read noise by almost half. So, dynamic range is the same (or slightly less). Then, you add some other technology improvements such as a new DCS circuits, better ADC, microlenses, etc. and the dynamic range actually goes up.

If my guess is wrong, then it means that read noise and dynamic range have been improving only because of new and separate technology and not in part as a natural consequence of shrinkage.

Furthermore, I think the natural read noise reduction will become less effective as sizes get smaller (again, due to things that don't scale, such as trapped carrier RTS noise).

On a related note, a paper presented by G.Agranov entitled Super Small, Sub 2μm Pixels for Novel CMOS Image Sensors at the 2007 International Image Sensor Workshop prompted Eric Fossum (inventor of CMOS APS) to coin Agranov's Law: "More pixels still improve image quality in the presence of noise".

In the end though, it's total area of sensor used to capture light that really defines the maximal dynamic range of a sensor. What higher resolution does is allow you more flexibility to do some trading.

Well put!

[Graeme Nattress]

Theory is fun, but from measuring we get learning.

Graeme

[Daniel Browning]

However, unfortunately, many times the effect of SNR decrease from blurring of data because of the window size is not taken into account.

Hi Joofa! Thanks for the response. I'm afraid that I don't understand what you've said, exactly, or how/why it is occurs. Is it possible to break it down in simpler terms?

[Dj Joofa]

Hi Joofa! Thanks for the response. I'm afraid that I don't understand what you've said, exactly, or how/why it is occurs. Is it possible to break it down in simpler terms?

When you are downsampling, then at each pixel location you would assemble a bunch of close pixels derived from the original image and determine the value of the dowsampled pixel from this set. I meant the collection of this set as the "window". One simple way is that you average together all pixel values. However, better methods exist than simple average -- though, which ever linear method you pick, it is some sort of weighted averaging of these pixel values in the set/window.

So we note two things. A larger window/set size will:

(1) reduce noise better, but,
(2) blur image more

Therefore (1) and (2) are two contradictory goals and one can be increased on the expense of the other. Therefore, the optimization problem involves finding a good window size and its shape -- i.e, its height as it varies over the set.

Some close form results exist for certain window types. In typical downsampling implementations you would see specifying the type of window say, sinc, lanczos, etc., but not always the optimality is derived considering what should be its size? Normally, after selecting a particular window type, a fixed window length (typically small, a few pixels in each direction) is chosen heuristically. That approach works well in practise. However, at least, in theory, an optimal window size exists considering the SNR formulation.