Understanding Resolution

Demystifying Resolution
An image’s resolution is determined by the image’s pixel count and the bit depth of
each pixel.
Learning About Pixels
A pixel is the smallest discernible element in an image. Each pixel displays one color. A
pixel’s color and brightness range is determined by its bit depth. For more information,
see “Learning About Bit Depth” on page 38.
Pixels are grouped together to create the illusion of an image. On color displays, three
color elements (one red, one green, and one blue) combine to form a pixel. As the
number of pixels increases, the image’s detail becomes sharper, more clearly representing
the original subject. Therefore, the higher the pixel count, the more likely the displayed
image will look like the original subject.
Because so many pixels fit in even a small image, pixel count is often expressed in
megapixels (millions of pixels). For example, 1,500,000 pixels equals 1.5 megapixels.

Learning About Bit Depth
Bit depth describes the number of tonal values or shades of a color each channel in a
pixel is capable of displaying. Increasing the bit depth of color channels in an image’s
pixels exponentially increases the number of colors each pixel can express.
The initial bit depth of an image is controlled by your camera. Many cameras offer
several file settings; for example, DSLR cameras usually have two settings, allowing the
photographer to shoot an 8-bit JPEG file (with 8 bits per color channel) or a 16-bit RAW
image file (with 12 to 14 bits per color channel).
Image file types use static bit depths. JPEG, RAW, and TIFF all have different bit depths.
As you can see in the table below, the file type you shoot your images in dramatically
impacts the tones visible in your images.

Here’s a practical example of bit depth. To understand the effect of bit depth on an
image, look at the picture of the girl below, which is an 8-bit grayscale image. Her eye is
used to illustrate the effects that lower bit depths have on the resolution of the image.

Formats like JPEG use 24 bits per pixel: 8 bits for the red channel, 8 bits for the green
channel, and 8 bits for the blue channel. An 8-bit color channel can represent 256
possible values (28), while three 8-bit color channels can represent 16,777,216 values (224).
RAW image files also use three color channels. Because most RAW files have the capacity
to capture 12 to 14 bits per color channel, their range of colors is exponentially larger

The following example illustrates how increasing the bit depth of a pixel increases the
number of color values it can represent. Increasing the bit depth by 1 bit doubles the
number of possible color values.

How Resolution Measurement Changes from Device to Device
As you now understand, resolution in itself isn’t complicated; it simply measures how
much detail an image can hold. However, as resolution is described for different digital
devices—cameras, displays, and printers—the different units of measurement can be
confusing. A camera’s resolution is calculated by the number of megapixels (millions of
pixels) its digital image sensor is capable of capturing. A display’s resolution is expressed
in pixels per inch (ppi) or as a maximum dimension, such as 1920 x 1280 pixels. A printer’s
maximum resolution is expressed in dots per inch (dpi)—the number of dots it can place
within a square inch of paper. These changing units make it hard to keep track of the
resolution of your digital image as it moves from one device to another. Not only do the
units of measurement change, but the numerical values change as well.

Mapping Resolution from Camera to Printer
Tracking the changing units of measurement from camera to display to printer is
confusing. But without an understanding of how resolution changes between devices,
you can inadvertently compromise the quality of your images.
Camera Resolution
A camera’s potential resolution is measured in megapixels (the number of millions of
pixels used to record the image). The larger the number of megapixels, the more
information is stored in the image. The reason a camera has a potential resolution is
that lens quality, the ISO setting, and the compression setting can affect the quality of
your image. For more information on how a camera operates, see Chapter 1, “How
Digital Cameras Capture Images,” on page 7.
The number of megapixels a camera is capable of capturing can be used to roughly
determine the largest high-quality print that the camera is ultimately capable
of producing.

Display Resolution
The maximum number of pixels that can appear on a display’s screen determines its
maximum resolution. Most displays have a variety of resolution settings from which to
choose. For example, the 23-inch Apple Cinema HD Display has resolution settings
from a minimum of 640 x 480 to a maximum of 1920 x 1200 pixels. As a photographer,
you will want to operate your display at its maximum resolution setting. This ensures
that you see as much of the image as possible on your screen.

About the Differences Between CRT and Flat-Panel Display Resolutions
CRTs and flat-panel displays are not bound by the same resolution characteristics. CRT
displays are capable of resolution switching, so that the resolution you select is
displayed at the actual resolution, and the pixels are drawn properly and sharply at
any supported resolution. Flat-panel displays have only a single native resolution that
appears sharp and true, which is the maximum resolution. Choosing any other
resolution forces the entire screen image to be interpolated to that size, resulting in a
soft, or slightly blurred, image.

Printer Resolution
In the end, it’s the quality of the print that counts. The quality of the print is determined
by the combination of two factors:
- Image file resolution: The resolution of the image file is determined by the number of
pixels in the image and the bit depth of the pixels themselves. Obviously, the more
pixels the image file has, the more information it’s capable of displaying. However,
along with the number of pixels, the bit depth plays a large part as well. The greater
the bit depth, the more colors a pixel is capable of displaying.
For more information on bit depth, see “Learning About Bit Depth” on page 38.
- Printer resolution: A printer’s resolution is determined by how closely together it is
capable of placing dots on paper within a square inch, measured in dpi. A printer’s
maximum dpi value determines the highest-quality image it can print.

Calculating Color and Understanding Floating Point
As you’ve learned, digital devices translate color into numbers. Aperture calculates
color using floating point, a type of calculation that allows calculations to be
performed at a very high resolution with a minimum of error.
Learning About Bit Depth and Quantization
When you capture an image using a digital image sensor, the analog voltage values
have to be converted to digital values that can be processed and then stored. For more
information, see “Digital Image Sensor” on page 17. The process of converting an
analog voltage value to a digital value is known as digitization. In the process of
converting an analog voltage value to a digital representation, quantization must be
performed, converting the values to discrete numerical values. The accuracy of each
pixel’s value is determined by the length of the binary word, or bit depth. For example,
a 1-bit binary word can represent only two possible states: 0 or 1. A 1-bit system
cannot capture any subtlety because no matter what the tonal value is, a 1-bit system
can represent it either as 0 or 1 (off or on). A 2-bit binary word can represent four
possible states: 00, 01, 10, or 11. And so on. Most digital RAW image files capture a
minimum of 12 bits per color channel (4096 possible states), allowing for many subtle
degrees of tonal values to be represented. The more bits available for each sample, the
more accurately each color channel’s tonal value can represent the original analog
voltage value.
For example, suppose you use 128 numbers to represent the tonal values of color
channels in each pixel in an image within a range of 1 volt. This means your camera’s
analog-to-digital converter is precise to 1/128 of a volt. Any subtle variations in tonal
values that are more detailed than 1/128 of a volt cannot be represented, and are
rounded to the nearest 1/128 of a volt. These rounding errors are known as quantization
errors. The more the signal is rounded, the worse the quality of the image.

Learning About the Relationship Between Floating Point and Bit Depth
When you make multiple adjustments to a digital image, the adjustments are
mathematically calculated to create the result. Just as with analog-to-digital
conversions, there can be quantization errors when adjustments are calculated. For
example, consider the following calculation: 3 ÷ 2 = 1.5. Note that for the answer to be
accurate, a decimal point had to be added for an extra level of precision. However, if
the bit depth of your pixels does not allow this level of precision, the answer would
have to be rounded to either 2 or 1. In either direction, this causes a quantization error.
This is particularly noticeable when you try to return to the original value. Without the
precision of floating point, you’re left with 1 x 2 = 2 or 2 x 2 = 4. Neither calculation is
capable of returning the original value of 3. As you can see, this can become
problematic when adjustments require a series of calculations and each subsequent
value is inaccurate. Since a large number of calculations are required to perform
complicated adjustments to an image, it is important that the adjustments are
calculated at a significantly higher resolution than the input or output resolution in
order to ensure the final rounded numbers are more accurate.
In the example below, a green channel of a 24-bit pixel (with 8 bits per color channel)
is capable of displaying 256 shades of green. If an adjustment is made calling for a
calculation between the 167th and 168th color values, without floating point the
application would have to round to one or the other. The result of the final calculation
would be a color that is close but not accurate. Unfortunately, information is lost.

Understanding How Aperture Uses Floating Point
Internally, Aperture uses floating-point calculations to minimize quantization errors
when image adjustments are processed. Floating-point calculations can represent an
enormous range of values with very high precision, so when adjustments are applied
to an image, the resulting pixel values are as accurate as possible. Often, multiple
adjustments to an image create colors outside the gamut of the current working color
space. In fact, some adjustments are calculated in different color spaces. Floating point
permits color calculations that preserve, in an intermediate color space, the colors that
would otherwise be clipped.
When it’s time to print the image, the output file has to be within the gamut range of
the printer. A pixel’s tonal values can be processed with incredible accuracy and then
rounded to the output bit depth, whether onscreen or print, as necessary. The accuracy
is most noticeable when rendering the darker shades and shadows of the image. The
bottom line is that image processing using floating-point calculations helps produce
extremely high image quality.

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