Improving the display of raster data
ArcMap provides tools to enhance the display of a raster. This includes providing faster drawing methods, enhancements, and retaining calculated raster dataset statistics. For instance, you can change the brightness and contrast of your raster and display the raster transparently over other layers. The Image Analysis window and Effects toolbar provide quick access to modify some properties of your displayed raster data. There are also geoprocessing tools that can permanently improve the display of your raster data.
Using faster drawing methods
Accelerated renderer
When working with any raster layer, such as a raster dataset, mosaic dataset, or image service (not raster catalog), you can increase the layer's display performance using accelerated raster rendering. Using the accelerated renderer, you can smoothly and seamlessly pan and zoom around the data in the display.
Footprints or wireframe
When you add a mosaic dataset to ArcMap, it is added as a mosaic layer that appears in the table of contents as a special group layer with a minimum of three layers: Boundary, Footprint, and Image. You can uncheck the Image layer and check on the Footprint or Boundary layers to see either the extents of each raster or the extent of the mosaic dataset.
When you are working with raster catalogs, ArcMap can display your raster catalog as a wireframe (showing an outline of the dimensions for each raster dataset). To increase display efficiency, this occurs automatically if more than nine images are in the current extent. The default number of images can be manipulated on the Display tab of the raster catalog's Layer Properties dialog box.
Performance tuning for faster drawing
Creation of pyramids and overviews
The best way to improve and reduce the time it takes to display a large raster dataset is by creating pyramids. Pyramids are additional copies of your original raster dataset that have been successively resampled in decreasing levels of resolution, created as a .rrd file or a .ovr file, with the same file name as your raster dataset. ArcMap uses the appropriate level of resolution to quickly draw the entire dataset. Without pyramids, the entire dataset must be read from disk and resampled to a smaller size. You cannot build pyramids on a raster catalog; however, you can build them on each raster dataset within the raster catalog. With mosaic datasets, like a raster catalog, you can build pyramids for each raster dataset; however, you can also build overviews for the mosaic dataset, which are like pyramids.
Calculation of statistics
When a raster without previously stored statistics is introduced in ArcMap and statistics are needed to render the raster correctly, ArcMap functionality creates default statistics from a subset of the raster dataset used just to render it.
It is recommended that you pregenerate statistics to help render the raster using the full statistics and histogram of the raster dataset. To learn about statistics and how to generate them, see Raster dataset statistics.
Raster compression
Compressed data must be decompressed to draw to the screen; it can be slower than uncompressed data. The amount of time spent on decompression is often related to the compression ratio. The more highly compressed the raster, the longer it takes to decompress. There are many types of compression available for raster datasets.
Tile size
Note:This functionality is only available with (tiled) TIFFs, file geodatabases, and ArcSDE.
The tile size is used to control the number of pixels, specified in rows and columns and stored in each tile (or block). Each tile is stored as a binary large object (BLOB). By default, the tile size is 128 by 128 pixels, but the default can be modified by the user, if necessary. Changing the tile size does not tend to significantly improve performance, and for ArcSDE, changing the default can actually decrease your performance.
Enhancing the appearance of the raster data
Adjusting the brightness, contrast, and transparency
The Image Analysis window (and the Effects toolbar) allows you to interactively adjust the brightness (
), contrast (
), or gamma (
) of a raster layer or make the raster layer display transparently. These enhancements are applied to the rendered screen display, not to the original raster dataset values. Brightness increases the overall lightness of the image—for example, making dark colors lighter and light colors whiter—while contrast adjusts the difference between the darkest and lightest colors. Below is an example of adjustments made to the brightness and contrast of an image.
 |
The Transparency tool (
) allows you to see other data layers underneath the raster layer. Below, the top image does not use transparency; therefore, the hillshade obscures the underlying land-use layer. With transparency (bottom), the underlying symbology appears through the hillshade, yielding a three-dimensional effect.
 |
If your raster data represents continuous data, you can apply a contrast stretch to it based on the statistics of the raster dataset. A stretch increases the visual contrast of the raster display. You might apply a stretch when your raster display appears dark or has little contrast. For example, images may not contain the entire range of values your computer can display; therefore, you could stretch the image's values to utilize this range by applying a contrast stretch. This may result in a crisper image, and some features may become easier to distinguish.
Below is an example of a contrast stretch. Histogram A represents the pixel values in image A. By stretching the values (shown in histogram B) across the entire range, you can alter and visually enhance the appearance of the image (image B).
 |
Different stretches will produce different results in the raster display. You can experiment to find the best stretch for a particular raster dataset.
The types of stretches in ArcMap include a custom manual option or several standard methods. These standard stretches can be used with either the RGB Composite or Stretched renderer. The standard stretches are Standard Deviations, Minimum–Maximum, ESRI, Histogram Equalize, and Histogram Specification. You might use the Minimum–Maximum stretch to spread out tightly grouped values. The Histogram Equalize and Histogram Specification stretches obtain their values from your histogram manipulation. A two-standard deviation stretch is often used to brighten raster datasets that normally appear dark. Using the ESRI method requires raster dataset statistics (and its histogram). This method is useful in providing a good overall stretch with imagery, by preventing pixel values to be stretched to the extremes.
With any of these stretch methods, you can examine and modify a histogram and see basic statistics (such as minimum, maximum, mean, and standard deviation) about your data. You could utilize these statistics to emphasize a particular value or to examine distribution. When you adjust the histogram, you see multiple sets of vertical bars: the purple bars represent the current display values, and the gray bars represent your original values. When using multiband data in the RGB Composite renderer, the red, green, and blue bars represent the current display values.
You can also apply a gamma stretch to your raster data if you are stretching your data with the following contrast stretches: None, Standard Deviation, or Minimum-Maximum. When preparing raster data for computer display, the gamma refers to the degree of contrast between the mid-level gray values of a raster dataset. Gamma does not affect the black or white values in a raster dataset, only the middle values. By applying a gamma correction, you can control the overall brightness of a raster dataset. Gamma values lower than one decrease the contrast in the darker areas and increase the contrast in the lighter areas. This darkens the image without saturating the dark or light areas of the image. This helps bring out details in lighter features, such as building tops. Conversely, gamma values greater than one increase the contrast in darker areas, such as shadows from buildings. Gamma values greater then one can also help bring out details in lower elevation areas when working with elevation data. Additionally, gamma changes not only the brightness but also the ratios of red to green to blue.
In the example below, you can see the effect of adjusting the gamma values used to display a raster dataset. Each of these values was added to the red, green, and blue bands. By applying different values to each band, you could adjust the degree of red, green, and blue.
 |
If you have stretched the entire histogram of your raster dataset and it still isn't displaying with enough contrast, you can try creating a stretch based on the pixels within the display's extent. You can do this in two ways:
- Check DRA on the Image Analysis window.
- Open the raster dataset Layer Properties dialog box; click the Symbology tab; and, for either the Stretched or RGB Composite renderer, you can change the Statistics drop-down to From The Current Display Extent.
This option allows only the statistics from the pixels within the display extent, rather than the entire raster dataset, to be used to calculate the contrast stretch. Since a smaller number of cell values exist in the display extent, a smaller range of cell values will most likely be used; this allows a greater contrast stretch. Each time the display extent (or location) changes, the raster dataset could display differently, because the contrast stretch calculated for the cell values in the display could change. If you want to apply a specific set of statistics to a certain extent, you can do this through the Custom Statistics option.
Display resampling
Resampling your raster dataset alters the way in which the raster dataset is displayed. Resampling is the process of interpolating new cell values while transforming your raster dataset when it undergoes a geoprocessing function or when it changes coordinate space.
The four resampling techniques are nearest neighbor, bilinear interpolation, cubic convolution, and majority. By default, ArcMap uses the most efficient resampling technique, nearest neighbor resampling.
For discrete raster datasets, such as those found in classified imagery including land-use maps or soil maps, the nearest neighbor and majority resampling algorithms are most appropriate. Nearest neighbor assigns the closest cell value to the pixel. Majority assigns the most popular value within the filter window, giving a smoother look.
For continuous raster datasets, such as a satellite image, an elevation model, or aerial photos, bilinear interpolation or cubic convolution are more appropriate. Bilinear interpolation creates a smooth-looking result. Cubic convolution creates a sharper-looking result but takes more processing time.
For ERDAS Imagine raster formats that have color maps, a bilinear interpolation resampling is possible. The process in this display resampling method requires converting the pixels to an RGB value, then using the resampling method.
The diagram below shows an example of display resampling. The image on the left shows the original raster and the new position of the raster (outline of the raster). The center image shows how the nearest neighbor resampling technique would resample the data. The image on the right shows how bilinear interpolation would resample the raster.
 |
Changing the display of the background
Sometimes, there are homogeneous areas in a raster dataset that you do not want to display. These can include borders, backgrounds, or other data considered to not have valid values. Sometimes, these are expressed as NoData values, although at other times, they may have real values.
Backgrounds and outlines can often be the result of georeferencing your raster dataset. If your raster data has a background, border, or other NoData values, you can choose not to display them or choose to display them as a particular color.
All renderers allow you to set the NoData value to a color or No Color, while the Stretched renderer allows you to identify a specific background value and display color or No Color.
The images below show a NoData area with a black background and that same area using No Color.
 |
The image on the left shows a no data area with a black background, and the image on the right shows that same area using No Color. |