Denary or Decimal
Base 2. Used by computers to represent all data and instructions. Uses 1s and 0s to powers of 2 to represent whole numbers.
Base 16. Used in computing because more values can be represented by fewer characters. This makes it easier for humans to read and understand.
Converting Between Number Bases
Denary to Binary
Divide by 2, then read the remainders backwards.
Binary to Denary
Multiply the binary numbers (i.e. every single digit) by the relevant place value, then add all of these together.
Denary to Hexadecimal
Divide by 16, then read the divisors and remainder backwards. Then convert digits to hex digits.
Hexadecimal to Denary
1. Separate the hex digits
2. Convert each digit to binary
3. Concatenate, then convert to denary
Binary to Hexadecimal
Convert to denary, then hex.
Hexadecimal to Binary
Convert to denary, then binary.
Units of Information
A single binary digit.
ASCII vs Unicode
What is ASCII?
A character set that uses 7 bits, so can represent up to 128 characters; this means that only Latin letters can be used (i.e. characters/letters from other languages can't be represented). However, it takes up less space that Unicode.
What is Unicode?
Unicode is also a character set, but it can represent many, many values, including non-Latin-based languages.
A single point in a graphical issues. Short for 'picture element'.
A grid of pixels, with each pixel represented by a binary number.
Number of colours that can (not necessarily are) be represented in an image, and the corresponding number of bits needed to represent each pixel (e.g. 2 bits for 4 colours). The greater the colour depth, the bigger the file size.
How much detail there is in an image. The more pixels per inch, the higher the resolution. The higher the resolution, the bigger the file size.
Gives the software the information needed to display the image properly (size, resolution depth, colour depth).
Bitmap file size = width x height x colour depth (in bits)
Data compression is used to reduce file size, which means that they take up less storage space. Lossy compression is where some data is removed - this means that an image would lose some detail. Lossless compression preserves all of the information.
Run length encoding (RLE) uses data frequency pairs to reduce the amount of data stored. It does so by stating the character and then the length of the run. Example:
1001 1111 0101 can be shown as 1 1 2 0 5 1 1 0 1 1 1 0 1 1
Huffman coding is more efficient than RLE. It is also lossless. It finds the frequency of each data item to create a Huffman tree, which assigns the most frequent items the shortest code. When you move down a branch to the left, a 0 is assigned. When you move to the right, a 1 is assigned.
total bits needed = number of bits needed per character x number of characters
A measure of amplitude at a given point. Used to convert an analogue wave into a digital format.
The number of samples taken in a second. Measured in Hertz.
The number of bits per sample.
The number of bits used per second of the audio. Usually measured in kilobits per second (kbps).
File size (bits) = sampling rate x resolution x lenght of recording (seconds)