Epic is the Ultimate "Film Slayer".... IF

[Phil Bates]

If there was no Phantom, film's last hold out for me would be frame rate. When I shoot on film, I rent a 435 with the capability to shoot 160fps. I am hoping Epic can do better than that at 2K.

Phil
www.artbeats.com

[Tom Lowe]

By the way, Tom, I really enjoyed your Joshua Tree astrophoto timelapse. Last time I was there I had telescopes but no cameras. Next weekend I'm shooting some star-and-landscape timelapse in the Oregon desert with a 5D, 20D, 350D, and the 24mm f/1.4.

very cool. my mom lives up in the Salem area, so I was going to check out the deserts there late this summer. do me a favor and post the results from that shoot here or over at the timescapes forum (see my signature) if you get a chance. i'll be curious to see how your shots turn out. good luck!

[Dan Hudgins]

It seems to me that Epic is the perfect storm to finally put chemical film out to pasture, in much the same way that high-end DSLRs did over the last few years for stills. It will shoot a "real" 4K, RAW, s35mm, cine glass, overcranking onboard, solid-state or harddrive recording, etc.

The ONE THING Epic needs to do in order to fulfill it's destiny, though, is match (or come close to) chemical film's dynamic range. Aside from nostalgia, DR is really the last leg film is standing on right now, IMO.

Similar to Clinton '92's precept "It's the economy, stupid," the Epic team should have a poster on the wall reading, "It's the dynamic range, stupid." :weight_lift:

With a sensor upgrade next year, I think Red One will do the same.

If you put two sensors in and a 45 degree Pellicle mirror you can overlap the curves and get more than 20 stop range to the tones, in other words more than the lens can take even with good coating. You could make a cemented lens that might make 18 stops for the camera. The image would look flat though on the screen since that dynamic range would need to be put in what can be projected.

I went through this with my brother using special films to get detail in the shadow under a car and in the reflections of the sun on its chrome at the same time, after all the work to get that he said it looked too flat and he wanted more contrast! Be careful what you ask for!

You can see what this would look like, make two shots with your RED ONE (tm) one +8 stops and one -8 stops, then use my fuse command to mix the two images together. To get better results the top end should be rolled off before you mix the images so there is not a hard clip, and the shadow should be rolled off so the blocking does not get mixed in, i.e. two S curves before mixing the frames.

There is no reason that they cannot make the Epic have a 18 stop or better range with two sensors since the streaking issue for the hot low end sensor seems under control and they know how to make a IR free ND filter for the high end sensor.

If they use 6 mono sensors they can make a camera with better color and more range than film, that WOULD end the issue once and for all time(?)

[Daniel Browning]

Daniel, in general there is a trade off between resolution and dynamic range. However, it is not always clear how to achieve the most optimal trade off.

I think the trade off is pretty clear. Image noise, for a constant size image, has decreased far more in recent years due to higher numbers of efficient pixels, than it has for reducing noise at the pixel level. You can see it by looking at any given sensor size and comparing performance at the pixel and image level. Both have improved, but the latter has improved faster and has more proven future potential (on 1/1.7" now, and hopefully in larger sensors in the future).

I don't think the above example is a good reflection of what you want to say. [...] You see the problem, you got back to where you started from. [...] However, the difference here is that unlike the first case where the removed high-frequencies were mostly noise, in this case it is noise + some actual signal spectrum. Therefore, the image becomes a little blurred / smoothed, and that is the price you pay for downsizing.

You're quite right. Thank you for pointing out my mistake.

Now that is a separate question that is the blurred downsized image still better in visual quality than a (smaller) sized image of the same size as the downsized image, i.e., acquired with a sensor with fewer number of photosites?

The downsize also has the benefit of removing the softening effect of the OLPF (to the degree that it is resized by the amount of the optical filtration), whereas the native image does not.

There are, of course, a great variety of downsizing and sharpening algorithms and their varied effect on subject matter, depending on the sizes and ratios involved and individual taste. And, as always, YMMV; the FZ50, for example, bungled the analog ISO so that 200 pushed three stops has far less noise than the analog 1600.

There are a lot of same-sized sensors out there, so it's easy to make your own comparison. To me, the downsized photos have clearly better detail and noise characteristics.

[Dj Joofa]

I think the trade off is pretty clear. Image noise, for a constant size image, has decreased far more in recent years due to higher numbers of efficient pixels, than it has for reducing noise at the pixel level.

Daniel, it is a simple matter if you are doing things manually as many software provide fixed length filters with fixed co-efficients with a few selectable options, and even then they are not optimal.

An automated software that is always "optimal" without manual supervision is a whole different beast. Before we go forward we need an understanding of what is the definition of being "optimal?" And, for that we are going to dig on the surface of some estimation theory here.

Whenever there is a mentioning of the word "optimal", it should all always come with the question but in what sense? And, typically answer boils down to a certain metric (objective function).

Suppose you are downsizing. So you are given a filter and at each location you convolve it with certain neighboring pixel values. Convolution basically means that you multiply filter coefficients with pixel values and sum them up. Now this step can be seen as linear prediction in the classical estimation theory consideration:

Given n samples (pixels), to go with a filter with n number of co-efficients, with what is the best estimate of a quantity (your current pixel value) at hand. Least squares is a widely used metric in signal processing. Now under certain assumptions (which are very realistic) the arithmetic mean is the least square estimator for a number of samples. Hence, an optimal estimator in a widely used sense.

However, we know that a filter that downsamples by averaging has poor visual response. Why did that discrepancy happen? Because, this particular estimator did not employ the photometric visual response.

If you are going to estimate a quantity from n samples with a linear combination such as c_i * x_i, where, c_i are co-efficients, and x_i are samples, and i are index, then based upon the values of c_i you can realize many different types of filters.

All c_i = 1/n gives a mean filter, just seen above. You can even have a (truncated) sinc filter, lanczos, etc. However, we just saw that in the least squares sense mean is the best one. Even better than lanczos?

Interestingly, though least square sense gave you a sense of the nature of the estimator, it did not say that th co-efficients c_i are the "best" ones. It just minimized the objective function in terms of "error". If you have some a priori notion about the probability distribution of c_i's then you can even use a Bayesian estimator, for a quadratic loss function for finding a set of c_i's that are closer to their "actual unknown values."

Now to make matters more complicated, if we assume stochastic behavior, then the beast starts getting unmanageable, as a whole new set of filters pop up, Wiener filter, Kalman filter, etc.

None of them is particularly optimized for visual response. On the other hand those filters that work visually may not be the most optimal under certain sense.

The problem then boils down to incorporating photometric visual weights into objective functions. Now a whole, separate area has sprung up, as we are not always clear that what is the best way that such visual weights can be incorporated into a purely number crunching statistical metric.

I shall leave it at there as it is already giving me nasty aftertastes.

[Daniel Browning]

Now to make matters more complicated...

That whooshing sound was your post going over my head. :)

[Radoslav Karapetkov]

What about randomized pixels?

That's another characteristic of film that I don't see in digital.

Is there some good software solution to achieve the random-pixels effect of film?

[Steven Caesare]

That is not a fact, that is an assumption. If you were designing photosites, you would want to increase this ratio, lower the area, and decrease the noise floor.

Isn't the light intensity (i.e. photon count per unit of surface area) determined by how much light your lens gathers and focuses on a given area?

So if you maintain the same sensor size (i.e. surface area), and use the same lenses, then how can the sensor's photosite size/count change that intensity?

[Steve White]

So if you maintain the same sensor size (i.e. surface area), and use the same lenses, then how can the sensor's photosite size/count change that intensity?

The photosites can't change the intensity, they measure it. Every photosite you have can measure the intensity over some absolute range, with some uncertainty. For example, you could have a photosite that can generate a signal from 0 to 1 Volt. This is your dynamic range. This signal is sampled by a Analog to Digital converter with some bit-depth - say 4096 values (12-bit). These 4096 divisions are your precision.

Even if no desired photons struck the photo-site, you would still get a signal, say 100 +/- 50 mV. In this case, the 100 mV is called the noise floor, a signal value below this is not terribly useful, since you have difficulty telling if it's signal or not... so you would probably try and crush the noise to black. To do this, you would probably need to sluff off 150 mV or so, leaving you with 850 mV of useful information. You still have the +/- 50 mV noise in the rest of the image though.

In order to improve the dynamic range of a photosite, you want to do several things.
First off, 0 to 1 Volts is your total dynamic range. If a single photon generated 1 mV, then you could collect 1,000 photons. If it generated 1 μV, you could collect 1,000,000 photons. Alternatively, you could invert the math, and sample 1000 V. You can guess which approach uses less power. Therefore, if possible, you want to decrease the sensitivity/gain. Low sensitivity sounds bad, but it isn't if you also have low noise and high precision.

Next up, you want to decrease the +/- 50 mV uncertainty. By reducing your noise variance, you effectively lower the noise floor. If you brought the uncertainty down to +/- 10 mV, you would how have 890 mV of useful information. This has the added benefit of reducing your image noise too.

Finally you want to decrease the noise floor. If you got it down to +/- 20 mV, you would now have about 970 mV of useful information.

Once you optimize your sensor to a noise floor and uncertainty on the order of 1/4096 of your total dynamic range, you increase the bit-depth of your D/A and start over.

This analysis assumes that the signal you get off your photosite is linearly proportional to number of "photons". I'm pretty sure CMOS and CCDs are linearly proportional to some measure of intensity per unit area. These assumptions don't really work for film due to the way grains collect light.

[Steven Caesare]

Right. They can only measure.

Hence the example of concern of a smaller site being "overloaded" by 100 photons is offset by the fact that the site is smaller, and fewer photons fall on it.