Data Representation

Description

Mind Map on Untitled, created by Elliott Powell on 13/12/2016.
Elliott Powell
Mind Map by Elliott Powell, updated more than 1 year ago
Elliott Powell
Created by Elliott Powell about 8 years ago
19
0

Resource summary

Data Representation

Annotations:

  • Data Representation
  1. Images as binary
    1. Images are stored as a series of pixels
      1. We mostly use bitmap images made up of tiny little dots called pixels
        1. The colour of a pixel is represented in a binary code
          1. Black and white images only use two colours meaning they need on,y 1 bit to represent each pixel
            1. To make a great range of shades and colours increasing the number of bits for each pixel
            2. Increasing the colour depth and resolution increases the file size
              1. Colour depth is the number of bits used for each pixels
                1. Total number of colours = 2n (n = number of bits per pixel)
                  1. The resolution is the density of pixels in an image e.g how many pixels are within a certain area
                    1. The higher the resolution, the more pixels in a certain area and that means the better quality of image
                      1. Increasing the resolution or the colour depth means there are bits in an image, improving the image quality but also the file size
                      2. Devices need metadata to display images
                        1. Metadata is the information stored in an image file which helps the computer recreate the image on screen from the binary data in each pixel
                          1. Metadata includes a image... File format, height, width, colour depth and resolution
                            1. Without metadata we wouldn't be able to display images on a screen as intended
                          2. Sorting sound
                            1. Converting analogue to digital is called sampling
                              1. All sound is recorded into a microphone as an analogue signal
                                1. Analogue signals are pieces of changing data
                                2. Sampling intervals
                                  1. The gaps between each of the points where the analogue is sampled
                                    1. Sampling frequency (or sample rate) is how many samples you take in per second
                                      1. Bit rate is the number of bits used per second of audio
                                        1. Bit rate = sampling frequency x sample size
                                        2. Increasing the sample frequency means the analogue recording is sampled more often the sampled sound will be better quality
                                          1. Increasing sample frequency and sample size will increase the bit size
                                        3. Characters as Binary
                                          1. Binary can be used to represent characters
                                            1. Alphanumeric characters are used to make words and strings including upper and lower case letter, digits 0-9 and a range of symbols
                                              1. Computers cannot directly process all these symbols so they have to turn them into binary
                                                1. Characters Sets
                                                  1. A collection of characters that a computer recognises from their binary Representation
                                                    1. ASCII The most commonly used character set in the English speaking world
                                                      1. Every ASCII character is given a 7bit binary code meaning that it can represent 128 different characters
                                                      2. EXTENDED ASCII
                                                        1. Gives an 8 but binary code which allows for 256 characters to be represented
                                                          1. Particularly useful for languages that include ascents
                                                          2. UNICODE
                                                            1. Tries to cover every possible character
                                                              1. Uses 16 to 32 bit binary codes
                                                                1. Covers every single language
                                                      3. Binary
                                                        1. Counting in binary is like counting in denary
                                                          1. Binary only uses two digits 0 and 1 (base 2)
                                                            1. The place values in binary increase in powers of 2 ( 8,4,2,1)
                                                              1. Binary numbers are easier to convert using tables.
                                                                1. Convert binary to denary by subtracting
                                                                2. We add Binary numbers using column addition
                                                                  1. Binary numbers only uses 0s and 1s therefore it's easy to do 0+0 = 0 etc but for 1+1 we write 10
                                                                    1. Sometimes we can come across overflow errors were the final binary number has too many numbers, the computer stores them elsewhere
                                                                    2. Binary shifts
                                                                      1. Binary shifts can be used to multiply or to divide by 2
                                                                        1. Gaps at the beginning it ends are filled with 0s
                                                                          1. Lefts shifts multiply meaning we add 0s on the right
                                                                            1. Right shifts divide meaning we add 0s to the left
                                                                        2. Hexadecimal Numbers
                                                                          1. Hexadecimal numbers are shorter than binary numbers
                                                                            1. Hexadecimal uses 16 different digits hence why it's called base 16
                                                                              1. 0-9,A-F
                                                                            2. Convert Binary to Hex by splitting it into nibbles
                                                                              1. For Hex to Binary, use each characters Denary value
                                                                                1. For the opposite way convert each hex character into binary
                                                                              Show full summary Hide full summary

                                                                              Similar

                                                                              Adding whole numbers L1
                                                                              Lee Holness
                                                                              Data representation
                                                                              Brodie McMeowface
                                                                              Data Representation
                                                                              zeligho9
                                                                              GCSE AQA Computer Science - Data Representation
                                                                              cc .
                                                                              Data Representation
                                                                              N Wright
                                                                              Data Representation
                                                                              James Swire
                                                                              Representing sound
                                                                              Armando Cotugno
                                                                              data representation: images
                                                                              kiana se
                                                                              Computing Revision YR8
                                                                              Darius C
                                                                              Data Representation
                                                                              mj.bloomfield
                                                                              Key Terms
                                                                              jramsden09