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An overview of raster storage settings

Raster storage environment settings can be used to adjust the default compression type, the default settings for pyramid creation and calculating statistics, the default tile size, the default resampling method, and the NoData mapping method to be used by geoprocessing core raster tools.

Geoprocessing extension toolsets such as 3D Analyst, Geostatistical Analyst, and Spatial Analyst will not honor all raster storage settings. Refer to the Help for each tool to see specifics on which environments are honored.

Not all settings apply to all storage types. Refer to the Raster storage matrix (below) for more details. File Group 2 consists of ERDAS IMAGINE files. All remaining ArcGIS-supported raster file formats fall into File Group 1.

Raster storage matrix

Storage settingsFile Group 1File Group 2personal GeodatabaseFile GeodatabaseEnterprise Geodatabase

Pyramids

yes

OVR file

yes

RRD type

yes

RRD type

yes

yes

  • Resampling

yes

yes

yes

yes

yes

  • Levels

yes

yes

yes

yes

yes

  • Skip first

yes

no

no

yes

yes

Raster statistics

yes

yes

yes

yes

yes

  • skip factor

yes

yes

yes

yes

yes

  • ignore value

yes

yes

yes

yes

yes

Compression

yes*

yes

RLE compression

yes

yes

yes

  • LZ77

yes*

no

yes

yes

yes

  • JPEG

yes*

no

yes

yes

yes

  • JPEG 2000

yes*

no

yes

yes

yes

Tile size

TIFF only

no

no

yes

yes

Raster storage matrix
Note:

Compression is dependent on the type of file format. Refer to the Supported raster dataset file formats to see which file formats are able to support compression.

Pyramids

Pyramids are reduced-resolution representations of your dataset. They can speed up display of raster datasets by retrieving only the data that is necessary at a specified resolution. By default, pyramids are created for raster datasets by resampling the original data. There are three resampling methods available: nearest neighbor, bilinear, and cubic.

If you uncheck Build pyramids, pyramids will not be created with the output raster. Not building pyramids saves storage space but will lead to slower display speeds, especially for larger raster datasets.

You have the option to skip the first pyramid level. Skipping the first pyramid level will save a little bit of disk space but will slow down the display when you are viewing small scales. Alternatively, you can define the number of levels, but this may affect the speed when viewing at a very large scale.

The default is nearest neighbor. It works for any type of raster dataset. Use nearest neighbor for nominal data or raster datasets with color maps, such as land-use data, scanned maps, and pseudo color images.

Use bilinear interpolation or cubic convolution for continuous data, such as satellite imagery or aerial photography.

If the raster pyramids are built as overviews (OVR), then it is also possible to compress the pyramids with either LZ77 or JPEG. If the pyramids can only be built as a reduced-resolution dataset, then no additional compression options are available.

Statistics

The Statistics option enables you to build statistics for output raster datasets. Statistics are required for your raster dataset to perform certain tasks in ArcMap or ArcCatalog, such as applying a contrast stretch or classifying your data. It is not essential to build statistics if they have not already been calculated, since they are calculated the first time they are needed. However, it is recommended that you calculate statistics for your raster datasets before using them if you want to use certain features that require statistics. The default display of your raster will be improved in most cases if statistics have already been calculated, because a standard deviation stretch is applied if statistics are present.

Setting a Skip factor allows you to speed up the process of calculating statistics by skipping pixels. The Skip factor does not apply for Grid datasets.

Values you set to ignore will not participate in the statistics calculation. Normally, you may want to ignore the values of the background.

Compression type

The compression type setting is used by any tool whose output is a raster dataset. There are nine different compression methods available for geoprocessing tools. Of these compressions, four types of compression are supported when loading rasters to a geodatabase: LZ77, JPEG, JPEG 2000, and NONE.

Valid compressions for each pixel depth

CompressionPixel Depth (8 bit)Pixel Depth (16 bit)Additional information

LZ77

Yes

Yes

Any pixel depth

LERC

Yes

Yes

As pixel depth increases, so does the efficiency of the compression algorithm

JPEG

Yes

Only 12-bit data; stored as 16-bit data

JPEG_YCbCr

Yes

No

JPEG2000

Yes

Yes

PackBits

Yes

No

1-bit to 8-bit data

LZW

Yes

Yes

Any pixel depth

RLE

Yes

Yes

Any pixel depth

CCITT_G3

No

No

Only for 1-bit data

CCITT_G4

No

No

Only for 1-bit data

CCITT_1D

No

No

Only for 1-bit data

Valid compressions for each pixel depth

LZ77 (the default) is a lossless compression that preserves all raster cell values. It uses the same compression algorithm as the PNG image format and one similar to ZIP compression. As you can rely on the pixels not changing their values after you compress them, use LZ77 for performing visual or algorithmic analysis.

JPEG is a lossy compression, because raster cell values may not be preserved after compression and decompression. It uses the public domain JPEG (JFIF) compression algorithm and only works for unsigned 8-bit raster data (single-band grayscale or three-band raster data).

JPEG_YCbCr is a lossy compression using the luma (Y) and chroma (Cb and Cr) color space components.

JPEG 2000 uses wavelet technology to compress rasters, so they visually appear lossless, meaning that although the cell values do get manipulated, the differences between the original and the same raster with compression are not easily distinguishable. Use JPEG or JPEG 2000 for rasters that are meant as pictures or backdrop imagery.

If JPEG or JPEG 2000 is selected, you can also set the compression quality to control how much loss the image will be subjected to by the compression algorithm. The values of the pixels of an image compressed with a higher compression quality will be closer to those of the original image. Valid value ranges of compression quality for JPEG are from 5 to 95. Valid value ranges for JPEG 2000 are from 1 to 100. The default compression quality is 75. The amount of compression will depend on the data and compression quality. The more homogeneous the data, the higher the compression ratio. The lower the compression quality, the higher the compression ratio. Lossy compression normally results in higher compression ratios when compared to lossless compression.

The primary benefits of compressing data are that compressed data requires less storage space and data display times will be quicker, as there is less information to transmit.

Tile size

The tile size setting is used by any tools that create raster datasets and are stored in blocks.

The default tile size is 128 by 128, which is good for most cases. However, if the tile size is too big, you will bring up more data than is needed each time you access the data. For example, you want to display a window of 100 by 100 and it only covers one tile. If you set the tile size to 512, you need to get a tile of 512 by 512 pixels. If your tile size is set to 128 by 128, you'll bring up less extra data if the display window is 100 by 100.

Resampling Method

Resampling is the process of interpolating the pixel values while transforming your raster dataset. This is used when the input and output do not line up exactly, when the pixel size changes, when the data is shifted, or a combination of these.

  • Nearest—Performs a nearest neighbor assignment and is the fastest of the interpolation methods. It is used primarily for discrete data, such as a land-use classification, since it will not change the values of the cells. The maximum spatial error will be one-half the cell size.
  • Bilinear—Performs a bilinear interpolation and determines the new value of a cell based on a weighted distance average of the four nearest input cell centers. It is useful for continuous data and will cause some smoothing of the data.
  • Cubic—Performs a cubic convolution and determines the new value of a cell based on fitting a smooth curve through the 16 nearest input cell centers. It is appropriate for continuous data, although it may result in the output raster containing values outside the range of the input raster. It is geometrically less distorted than the raster achieved by running the nearest neighbor resampling algorithm. The disadvantage of the Cubic option is that it requires more processing time. In some cases, it can result in output cell values outside the range of input cell values. If this is unacceptable, use Bilinear instead.

NoData

Use this environment when the NoData value from your input needs to be transferred to your output raster. This setting allows you to specify which value you use to designate as the NoData value in your output.

  • None—There will not be any NoData value rules in place. If your input and output have the same value range, NoData will be transferred over without any changes. However, if your value range changes, there will be no value for NoData in your output. This is the default method.
  • Maximum—The maximum value in the output data range will be used as your NoData value.
  • Minimum—The minimum value in the output data range will be used as your NoData value.
  • Map values up—The lowest value in the range will be promoted and the lowest will become NoData. If the data is unsigned, the value of zero will become one, the NoData value will be zero, and the rest of the values remain the same. If the data is signed, the lowest value in the range will be promoted and the lowest will become NoData. For example, with 8-bit signed integer data, -127 will become -126, and the NoData value will be -127.
  • Map values down—The NoData value will be the maximum value in the data range, the highest value of the data range will become one value less, and the rest of the values remain the same. For example, with 8-bit unsigned integer data, the NoData value will be 255, the value of 255 will become 254, and the rest of the values will remain the same.
  • Promotion—If there is a NoData value outside the input's data range, the pixel depth of the output will be promoted to the next available level, and NoData will take the maximum value in the new data range. For example, an 8-bit unsigned integer dataset that requires the 256 value to be NoData will be promoted to a 16-bit dataset and the maximum value will become NoData. If there is a NoData value within the input's data range to be written to the output, or if there isn't any NoData, the pixel depth will not be promoted.

    If there is a NoData value outside of the input's data range, the pixel depth will be promoted to the next available level, and the Nodata value will be the one specified by the user. For example, an 8-bit unsigned integer dataset that requires 256 as NoData will be promoted to a 16-bit dataset and value 256 becomes the NoData value. If the NoData value specified is within the input's data range, the pixel depth will not be promoted for the output.