3.3 Fundamentals Of Data Representation

Image of grid of pixels, that’s a picture element, smallest area or bitmap graphic Each pixel represented by bit pattern (colour) Image size = WxH of pixels File size = colour depth x image size

More colours more colour depth more storage More pixels higher resolution more storage

Eg 20 colours 20-1 =19 19 in binary = 1111 = 4 bits to represent

microphone Transducer converts analogue sound into electrical signals Then converted to digital binary values (=sampling)

In bits Sampling rate per second x sample resolution x seconds

Modify data to Reducing file size Less storage capacity, Less time to transfer, less network bandwidth consumption

Lossy and lossless Lossy is irreversible and data is lost

Run length encoding eg (4 1), (2 0) 111100 Huffman coding those tree graphs + freq table

ASCII only has 7 bits so can only code for latin alphabet, 128 characters , so other alphabets are not represented for

unicode codes for 8 or 16 bits, so maximum can code for 65536 characters, more universal to represent for other alphabets beyond latin

microphone picks up sound waves converted to an electrical analogue signal value read and rounded binary representation stored

more measurements taken = less missing parts of original sound = more accurate

• microphone picks up sound waves, sampling rates at regular intervals • converted to electrical analogue signal • value read at specific point and rounded to a level • binary values stored

number of bits to represent a sound sample

number of samples taken per second

computer systems use binary storage sound is analogue / continuous