Raster Graphics: Understanding the Foundations of Raster Graphics in Computer Vision

By Fouad Sabry

What is Raster Graphics

In computer graphics and digital photography, a raster graphic represents a two-dimensional picture as a rectangular matrix or grid of pixels, viewable via a computer display, paper, or other display medium. A raster is technically characterized by the width and height of the image in pixels and by the number of bits per pixel. Raster images are stored in image files with varying dissemination, production, generation, and acquisition formats.

How you will benefit

(I) Insights, and validations about the following topics:

Chapter 1: Raster graphics

Chapter 2: Pixel

Chapter 3: RGB color model

Chapter 4: Vector graphics

Chapter 5: Dot matrix

Chapter 6: Rasterisation

Chapter 7: Framebuffer

Chapter 8: Truevision TGA

Chapter 9: Dots per inch

Chapter 10: Color depth

(II) Answering the public top questions about raster graphics.

(III) Real world examples for the usage of raster graphics in many fields.

Who this book is for

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Raster Graphics.

• Language: English
• Publisher: One Billion Knowledgeable
• Release date: May 13, 2024

Book preview

Chapter 1: Raster graphics

In computer graphics and digital photography, a raster graphic portrays a two-dimensional image as a rectangular matrix or grid of square pixels that can be viewed on a computer monitor, paper, or other display medium. Technically, a raster is defined by the image's width and height in pixels and the number of bits per pixel. Raster pictures are stored in image files whose distribution, creation, generation, and acquisition formats vary.

In the printing and prepress industries, raster images are referred to as contones (from continuous tones). In digital systems, however, line art is typically implemented as vector graphics.

Numerous raster manipulations transfer directly onto the mathematical formalisms of linear algebra, where the structure of mathematical matrices is of major significance.

The term raster comes from the Latin rastrum (a rake), which is derived from the verb radere (to sweep) (to scrape). It derives from the raster scan of cathode ray tube (CRT) television monitors, which paint the image one line at a time by magnetically or electrostatically guiding a focussed electron beam. It can also be used to refer to a rectangular grid of pixels. Currently, the term rastrum refers to a tool for making musical staff lines.

The tessellation of a plane into a two-dimensional array of squares, each called a cell or pixel, is the fundamental approach underpinning the raster data model (from picture element). In digital photography, the plane is the projected visual field onto the image sensor; in computer art, it is a virtual canvas; and in geographic information systems, it is a projection of Earth's surface. The resolution or support, the size of each square pixel, remains consistent across the grid. A gridding method could provide raster or grid-based data.

For each pixel, a single numeric value is then saved. This value is a visible color for the majority of photos, but alternative measurements are possible, including numerical codes for qualitative categories. Each raster grid has a unique pixel format, which specifies the data type of each integer. Common pixel formats include binary, grayscale, paletted, and full-color, where color depth controls the accuracy of the colors displayed and color space determines the range of color coverage (which is often less than the full range of human color vision). The majority of contemporary color raster formats express color with 24 bits (nearly 16 million different colors) and 8 bits (0–255) per color channel (red, green, and blue). Digital sensors used in remote sensing and astronomy are frequently capable of detecting and storing wavelengths beyond the visible spectrum; the large CCD bitmapped sensor at the Vera C. Rubin Observatory captures 3.2 gigapixels in a single image (6.4 GB raw) over six color channels that exceed the spectral range of human color vision.

The majority of computer images are saved in raster graphics formats or their compressed variants, such as GIF, JPEG, and PNG, which are widely used on the Internet. A raster data format is built on a (often rectangular, square-based) tessellation of the 2D plane into cells with a single value each. The two-dimensional array must be serialized to store the data in a file. In a row-major format, the cells along the first (typically top) row are listed from left to right, followed immediately by those of the second row, and so on.

In the illustration on the right, the cells of tessellation A are superimposed on the point pattern B, yielding an array C of quadrant counts reflecting the number of points in each cell. For visualization purposes, a lookup table was utilized to color each cell in image D. The numbers are presented as a serial row-major array:

1 3 0 0 1 12 8 0 1 4 3 3 0 2 0 2 1 7 4 1 5 4 2 2 0 3 1 2 2 2 2 3 0 5 1 9 3 3 3 4 5 0 8 0 2 4 3 2 8 4 3 2 2 7 2 3 2 10 1 5 2 1 3 7

To reconstruct the two-dimensional grid, the file must include a header section that specifies at least the number of columns and the pixel datatype (with the number of bits or bytes per value) so that the reader knows where each value stops and where to begin reading the next one. The number of rows, georeferencing parameters for geographic data, and other metadata elements, such as those provided by the Exif standard, may also be included in headers.

Raster grids with a high resolution include a big number of pixels and hence require a large quantity of memory. This has resulted in numerous techniques for compressing large data volumes into smaller files. The most prevalent approach is to search for patterns or trends in the pixel values and then save a parameterized version of the pattern in place of the original data. Run-length encoding (RLE), JPEG, LZ (the basis for PNG and ZIP), Lempel–Ziv–Welch (LZW) (the basis for GIF), and others are common raster compression algorithms.

Run length encoding, for instance, searches the array for repeated values and replaces them with the value and the number of times it appears. Therefore, the above raster would be represented as:

This technique is particularly effective when there are huge areas of equal values, such as in a line drawing; however, in an image, where pixels are typically slightly different from their neighbors, the RLE file could be up to twice as large as the original.

Some compression methods, including RLE and LZW, are lossless, meaning that the original pixel values may be completely reproduced from the compressed data. Other techniques, such as JPEG, are lossy since the parameterized patterns are merely an approximation of the original pixel values, and the latter can therefore only be approximated from the compressed data.

Software can rasterize (convert into pixels) vector images (line work) and vectorize (convert raster pictures into vector graphics) raster images. In both instances, some information is lost, although certain vectorization procedures, such as optical character recognition, can reconstruct pertinent information.

In the 1920s, the first mechanical televisions utilized rasterization concepts. Electronic television based on cathode-ray tube screens are raster scanned with horizontal rasters and raster lines painted left to right and top to bottom, respectively.

Modern flat-panel displays, such as LED monitors, continue to employ a raster technique. Each pixel on the screen correlates directly to a tiny number of bits in memory. Simply scanning through pixels and coloring them according to each set of bits refreshes the display. The refresh operation, which is time-sensitive, is frequently implemented by dedicated circuitry, which is frequently a component of a graphics processing unit.

Using this method, the computer is equipped with a section of memory that stores all the data to be presented. The central