images

• • Computers represent all data in binary, including images that are seen on a screen, TV or other output device
  • Images can be stored in binary as Bitmap or Vector
• • A bitmap image is made up of squares called pixels
  • A pixel is a single point / the smallest element of a bitmap image
  • Each pixel is stored as a binary code
  • Binary codes are unique to the colour in each pixel
  • A typical example of a bitmap image is a photograph
  • The more colours and more detail in the image, the higher the quality ofthe image and the more binary that needs to be stored
• • A vector image is drawn by the computer following precise mathematical instructions to create lines and objects
  • Only the mathematics used to create the image are stored
  • For example, to create a circle the data stored would be: Centre point(x, y coordinates) and Radius
  • Typical examples of vector images are logos and clipart
  • Vector images are infinitely scalable
  • Ideal for situations where the same image will be made bigger and smaller and a loss of quality is unacceptable. For example, the same logo used on both a pencil and a billboard
• resolution

  • Also called pixel density, is the number of pixels or dots in a unit / how tightly packed the pixels are
  • formula: height x width
  • higher = more detail (higher quality)
  • In a low-resolution image, the pixels are larger and therefore, fewer are needed to fill the space.
  • Resolution can also refer to the total amount of pixels horizontally in a display, such as:

  1. Computer monitors - 1440p means 1440 pixels horizontally
  2. TVs - HD (high definition) channels 1080p, 1080 pixels horizontally
  3. YouTube - video playback resolution from 144p (144 pixels horizontally) up to 4K
• colour depth

  • number of bits stored per pixel in a bitmap image
  • As colour depth increases, so does the amount of colours available in an image (more detail) (higher quality)
  • In a black & white image the colour depth would be 1 (2^1) to create a unique binary code for each colour in the image (1=white, 0=black)
  • The amount of colours can be calculated as 2^n (n = colour depth)
  • the RGB colour model uses 3 bytes (1 for each of RGB)

  
• file size

  • As the resolution and/or colour depth increases, the bigger the size of the file becomes on secondary storage
  • The higher the resolution, the more pixels are in the image, the more bits are stored
  • The higher the colour depth, the more bits per pixel are stored
  • file size (in bits): width x height x colour depth
  • in bytes = file size (in bits) ÷ 8
  • OR resolution x colour depth
• metadata

  • Metadata is data about data / additional data about a file stored with the image
  • although not required to display the image it provides context and information
  • Examples of metadata that can be stored are: Author, Date/Time created / taken, Location, Width & height of the image (resolution), Colour depth