Metering Modes

All Digital SLRs have a few different metering modes for different situations, and are very helpful for getting a correct exposure. In this Premium tutorial, we’ll be taking a look at how these different modes work, how to react when your meter gets it all wrong, and showing a few real-life examples of difficult metering situations.



All DSLRs have a few different metering modes for different situations and are very helpful to get a correct exposure. In this tutorial we’ll explore and explain:

• Matrix or Evaluative Metering
• Spot and Partial Modes
• Center Weighted Average

When Meters Get It All Wrong!

In-camera meters are designed to get a correct exposure on a mid-tone (think of green grass or blue skies), but more precisely an 18% reflective grey area. In most cases, this works out great since there are so many mid-tones in most scenes we shoot. But what if the predominate subject in our viewfinder is not a mid-tone?

We’ll go into detail on how to adjust and adapt in this situation, along with explaining just why the problem occurs. In addition to this, Peter shares a number of real-life examples and outlines what to watch out for when shooting.

MICROLENS ARRAYS

ou might wonder why the first diagram in this tutorial did not place each cavity directly next to each other. Real-world camera sensors do not actually have photosites which cover the entire surface of the sensor. In fact, they often cover just half the total area in order to accommodate other electronics. Each cavity is shown with little peaks between them to direct the photons to one cavity or the other. Digital cameras contain "microlenses" above each photosite to enhance their light-gathering ability. These lenses are analogous to funnels which direct photons into the photosite where the photons would have otherwise been unused.

Well-designed microlenses can improve the photon signal at each photosite, and subsequently create images which have less noise for the same exposure time. Camera manufacturers have been able to use improvements in microlens design to reduce or maintain noise in the latest high-resolution cameras, despite having smaller photosites due to squeezing more megapixels into the same sensor area.

DEMOSAICING ARTIFACTS

Images with small-scale detail near the resolution limit of the digital sensor can sometimes trick the demosaicing algorithm—producing an unrealistic looking result. The most common artifact is moiré (pronounced "more-ay"), which may appear as repeating patterns, color artifacts or pixels arranges in an unrealistic maze-like pattern:

                                              Second Photo at ↓ 65% of Above Size

Two separate photos are shown above—each at a different magnification. Note the appearance of moiré in all four bottom squares, in addition to the third square of the first photo (subtle). Both maze-like and color artifacts can be seen in the third square of the downsized version. These artifacts depend on both the type of texture and software used to develop the digital camera's RAW file.

BAYER DEMOSAICING

Bayer "demosaicing" is the process of translating this Bayer array of primary colors into a final image which contains full color information at each pixel. How is this possible if the camera is unable to directly measure full color? One way of understanding this is to instead think of each 2x2 array of red, green and blue as a single full color cavity.


                             >  




This would work fine, however most cameras take additional steps to extract even more image information from this color array. If the camera treated all of the colors in each 2x2 array as having landed in the same place, then it would only be able achieve half the resolution in both the horizontal and vertical directions. On the other hand, if a camera computed the color using several overlapping 2x2 arrays, then it could achieve a higher resolution than would be possible with a single set of 2x2 arrays. The following combination of overlapping 2x2 arrays could be used to extract more image information.












Note how we did not calculate image information at the very edges of the array, since we assumed the image continued on in each direction. If these were actually the edges of the cavity array, then calculations here would be less accurate, since there are no longer pixels on all sides. This is no problem, since information at the very edges of an image can easily be cropped out for cameras with millions of pixels.

Other demosaicing algorithms exist which can extract slightly more resolution, produce images which are less noisy, or adapt to best approximate the image at each location.

DIGITAL CAMERA SENSORS

A digital camera uses a sensor array of millions of tiny pixels in order to produce the final image. When you press your camera's shutter button and the exposure begins, each of these pixels has a "photosite" which is uncovered to collect and store photons in a cavity. Once the exposure finishes, the camera closes each of these photosites, and then tries to assess how many photons fell into each. The relative quantity of photons in each cavity are then sorted into various intensity levels, whose precision is determined by bit depth (0 - 255 for an 8-bit image).

Each cavity is unable to distinguish how much of each color has fallen in, so the above illustration would only be able to create grayscale images. To capture color images, each cavity has to have a filter placed over it which only allows penetration of a particular color of light. Virtually all current digital cameras can only capture one of the three primary colors in each cavity, and so they discard roughly 2/3 of the incoming light. As a result, the camera has to approximate the other two primary colors in order to have information about all three colors at every pixel. The most common type of color filter array is called a "Bayer array," shown below.

A Bayer array consists of alternating rows of red-green and green-blue filters. Notice how the Bayer array contains twice as many green as red or blue sensors. Each primary color does not receive an equal fraction of the total area because the human eye is more sensitive to green light than both red and blue light. Redundancy with green pixels produces an image which appears less noisy and has finer detail than could be accomplished if each color were treated equally. This also explains why noise in the green channel is much less than for the other two primary colors (see "Understanding Image Noise" for an example).

MORE :

BAYER DEMOSAICING
DEMOSAICING ARTIFACTS
MICROLENS ARRAYS