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Text and Images
Key Revision Points
Text - ASCII
 The problem:
 Representing text strings, such as “Hello world”, in a computer
 Character set maps letters (or symbols) to numeric values which can be stored as
binary numbers
 One common coding system/character set is ASCII
 7-bit code - 8th bit is unused (or used for a parity bit) – still requires one byte of storage
per character
 2^7 = 128 codes = 128 unique characters can be represented in the basic ASCII
character set
Text – Extended ASCII and Unicode
 Extended ASCII uses all 8 bits – 2^8 -128 codes = 128 unique characters can be
represented in the extended ASCII character set
 Still not enough e.g. no £ sign or “foreign” characters
 The Unicode character set uses 16 bits per character.
 Therefore, the Unicode character set can represent 216, or over 65 thousand characters
Text - Next In Sequence…
 “What is the binary value of the next ASCII letter in the sequence. A=1000001, B=1000010,
E=????”
 To get the answer:
 Convert to denary first
 E.g. 1000001=65, 1000010=66…
 Work out next denary number(s) in sequence e.g. C=67, D=68, E=69
 Convert to binary E.g. E= 69 = 1000101
Images
 Bitmap images are made up of individual pixels (picture elements).
 In memory or storage the colour of each pixel is represented as a binary number.
 The image is therefore stored as a series of binary numbers
 Colour depth or bit depth describes how many memory bits are used to store the colour of
each pixel
Images – Bit Depth
 The more bits you use for storing data about each pixel the more unique colours an image
can have:
 A 1 bit image can only have 2 colours
 An 8 bit image can have 256 colours
 A 24 bit image can have 16 million + colours!
Images – Bit Depth
1 bit – 2 unique colours
0
0
1
1
0
1
0
0
1
1
0
0
1
0
1
1
0
2 bit – 4 unique colours
0
00
01
01
00
01
10
11
01
01
11
10
01
00
01
01
00
4 bit – 16 unique colours
0000
0100
1000
1100
0001
0101
1001
1101
0010
0110
1010
1110
0011
0111
1011
1111
Formula
 Max. number of unique colours = 2 to the power of bits
 E.g. 4 bit image = 2 ^ 4 = 16 colours
 E.g. 8 bit image = 2 ^ 8 = 256 colours
Resolution
 Resolution is the number of pixels in an image.
 Higher resolution  better quality images
 Lower resolution  pixilation
 Measured in PPI (pixels per inch)
 40PPI means 1600 pixels in a one inch square
 80PPI means 6400 pixels in a one inch square
File Size
 The higher the bit (colour) depth and resolution the bigger the file
 Theoretically we can calculate the size of an image file e.g. 1 inch 40PPI image using a bit
depth of 4
 40 x 40 pixels = 1600 pixels in total. Each pixel requires 4 bits so 4 x 1600 = 6400bits
 To convert to bytes divide by 8 (8 bits in a byte). 6400 bits = 800 bytes
 In reality the file will be bigger as it also needs to contain metadata – data about the image
 File format
 Height
 Width
 Colour depth
 Resolution
Without this info the image wont be displayed correctly
Textbook
 Textbook pages 72-73