As we’ve seen in the previous posts and in one comment thread, the main reason – some say the only reason – to use the expose-to-the-right method is to achieve minimum noise. If you’re using an iPhone, that’s important all the time. With a point and shoot camera, it’s important most of the time. With a micro four-thirds camera, it’s important some of the time. With an APS-C sensor, it’s important in dim light. With a 24x36mm sensor, it’s rarely important, especially if you res your images down to 12 megapixels or less.
On a pixel-for-pixel basis, the sensor technology being equal, the signal to noise ratio (SNR) is proportional to the square root of the photosite area. This means the SNR is proportional to the pixel pitch.
It’s not really fair to compare noise in cameras with wildly varying resolution. Fortunately, we don’t have to. Consider two cameras using full frame sensors with the same technology, one 40 megapixels, and the other 10 megapixels. The sensor with more pixels will have half the SNR of the sensor with fewer, if the measurements are performed under the same conditions. If we res down the 40 megapixel to 10 megapixels, to a first approximation it will have the same SNR as the 10 megapixel camera. So, technology and output resolution held constant, the SNR of a camera is proportional to the linear dimensions (length, width, or diagonal – your choice, if the aspect ratio is the same) of the sensor.
So, if a full frame camera, measured under a standard set of conditions with its output file res’ed to a certain resolution has an SNR of x, an APS-C camera will have an SNR of 0.7x. A micro four-thirds camera will have an SNR of half of x. A Leica D-Lux 4 will have an SNR of one quarter x. An iPhone will have an SNR of one-eighth of x. Going the other way, a medium format camera will have an SNR of somewhat less than 2x, and a 4×5 scanning back will have an SNR of a little less than 4x.
Can we quantify the SNR effects of ETTR? Indeed we can. Let’s take an image that’s exposed perfectly to the right. No clipping or blown highlights, but information in the very top histogram bucket. Let’s pick a pixel group in that image, and measure its SNR. Let’s say it measures y. If we underexpose one stop from the perfect ETTR image, that pixel group will have an SNR of 0.7y. If we’re two stops under, the SNR is half of y. Four stops under, and it’s a quarter.
So, from a noise point of view, you can turn your full frame SLR into a micro four-thirds camera by underexposing by two stops, a point-and-shoot by underexposing by four stops, and into an iPhone by underexposing by six stops.
Admittedly, I’m painting with a truly broad brush here. The sensor technology in the iPhone is probably different from that in a D4 by more than just geometry. Still, I think it’s a useful way of looking at ETTR.
Then there’s the issue of having too little noise. There seems to be a groundswell of people saying digital is bad because it’s too good. A couple of representative examples:
Christian Popkes:“Digital photography is actually too perfect, too ideal – somehow sterile.” http://h41140.www4.hp.com/mac_connect/uk/en/christian_popkes.html
“Holger”: “Digital may be sharper, grain-free, flexible. But to me it simply is too perfect…” http://www.apug.org/forums/archive/index.php/t-68235.html
If you buy that point of view, then ETTR may be moving your work in the wrong direction.
There are valid times where ETTR can be important to use on APC and “full frame” cameras.
When the dynamic range of the scene is wide. You would then want to use ETTR Capture as much of the lower tones. The overall range could then be compressed in Lightroom.
Not as well known is using ETTR to maximize the exposure (best SNR) of a flat, narrow dynamic range scene. By pushing the exposure as far to the right, without blowing any highlights, you will have minimized noise. This is important if you want to spread out the tones to enhance the contrast.
I’ve got no argument with any of that. I was speaking in generalizations, and you’ve pointed out places where I was over-general. I expect that others will find fault with my admittedly greatly simplified SNR calculations.
Jim