What is raster compression?

The quadtree compression technique is the most common compression method applied to raster data. Quadtree coding stores the information by subdividing a square region into quadrants, each of which may be further subdivided in squares until the contents of the cells have the same values.

The size of raster image files is positively correlated with the number of pixels in the image and the color depth (bits per pixel). Images can be compressed in various ways, however.

Rasters are well suited for representing data that changes continuously across a landscape (surface). They provide an effective method of storing the continuity as a surface. They also provide a regularly spaced representation of surfaces.

Many people feel that they should only use image formats that use lossless compression. While lossless compression is superior for many kinds of images, it’s not necessary for many others. Basically, lossless image compression means all the data from the original file is preserved.

Lossy compression is most commonly used to compress multimedia data (audio, video, and images), especially in applications such as streaming media and internet telephony. By contrast, lossless compression is typically required for text and data files, such as bank records and text articles.

LZW compression is the compression of a file into a smaller file using a table-based lookup algorithm invented by Abraham Lempel, Jacob Ziv, and Terry Welch. Two commonly-used file formats in which LZV compression is used are the GIF image format served from Web sites and the TIFF image format.

Raster graphics are best used for non-line art images; specifically digitized photographs, scanned artwork or detailed graphics. Non-line art images are best represented in raster form because these typically include subtle chromatic gradations, undefined lines and shapes, and complex composition.

Vector graphics are digital art that is rendered by a computer using a mathematical formula. Raster images are made up of tiny pixels, making them resolution dependent and best used for creating photos. … Raster images are made of pixels, or tiny dots that use color and tone to produce the image.

• Simple data structures.
• Overlay and combination of maps and remote sensed images easy.
• Some spatial analysis methods simple to perform.
• Simulation easy, because cells have the same size and shape.
• Technology is cheap.

An example of discrete raster data is population density. Continuous data examples are temperature and elevation measurements. There are also three types of raster datasets: thematic data, spectral data, and pictures (imagery). … This example of a thematic raster dataset is called a Digital Elevation Model (DEM).

Disadvantages of Raster Data Structures: • The use of large cells to reduce data volumes means that phenomenonologically recognizable structures can be lost and there can be a serious loss of information • Crude raster maps are considerably less beautiful than line maps • Network linkages are difficult to establish • …

There are three types of raster data that can be stored in a geodatabase: raster datasets, raster catalogs, and raster as attributes. Raster datasets are single images that are stored in the database.

Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level. The reduction in file size allows more images to be stored in a given amount of disk or memory space.

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