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IGCSE Computer Science 2023-25 - (1) Data Representation - 1.2(a) Text, Sound and Images — Transcript

by Mr Bulmer's Learning Zone · 2,049 words · 362 segments · language en · Watch on YouTube

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0:00for this next video we're going to
0:01continue with data representation but
0:03we're going to move on to part two
0:05which is all about text sound and images
0:09um in particular this is broken into
0:10three specific parts
0:12character sets
0:14um working with ascii code and unicode
0:16and the representation of sound and the
0:19representation of bitmap images
0:21all these different file formats text
0:23sound and images um need to be converted
0:26into a binary format
0:28in order for a computer to understand
0:30and be able to work with it and this um
0:33this video hopefully will explain all of
0:36that to you
0:38so we start off with character sets
0:40ascii code and unicode but um the
0:44question is what are character sets well
0:47these are made up of all the letters the
0:48numbers and various symbols
0:51then that can be encoded inside a
0:52computer as patterns of binary digits
0:56but what does that mean
0:58well when you press a key key on your
1:00keyboard a number is generated that
1:03represents a symbol for that particular
1:05key this is called a character code a
1:08complete collection of characters is
1:10what's known as a character set now well
1:13what do i mean by that
1:14um for example if um we press the a
1:19the capital a on on your keyboard
1:22and the number 65 is generated now this
1:26cost is the idenary number and that is
1:28converted into binary which would be 0 1
1:311 lot of 64 0 0 0 0 0 and then 1 being
1:36the one so 65
1:38the ascii code system the american
1:41standard code for information
1:42interchange was set up um
1:45a long time ago back in 1963
1:47and it was used for use in the
1:49communication systems and in computer
1:52systems
1:53as you can see here i've i've
1:54highlighted the um character capital a
1:58from the keyboard
1:59which has a decimal value of 65 and an x
2:03decimal value of 41 and of course that's
2:05been converted into binary as you can
2:08see there
2:11um the standard ascii code character set
2:13consists of seven bits
2:15um seven bit codes
2:17so any a number from zero to 127 in
2:21general or
2:220 0 to 7f in hexadecimal so basically it
2:27allows for 128 different characters to
2:30be represented
2:31and characters that you can find on the
2:32standard keyboard
2:34also together with that
2:36um 32 control codes
2:39i'm going moving from
2:410 to 31
2:44and as you can see there
2:45um the backspace
2:47is represented by the
2:49number eight
2:51and the escape key on the keyboard is
2:53represented by the number 27 or a next
2:55decimal
2:561b
2:58now obviously um
3:00128 characters doesn't give us a lot of
3:02scope for storing numbers
3:04um symbols
3:06um letters etc so in the 1980s a version
3:11of ascii came out called extended ascii
3:14um which allowed for not seven bits but
3:16now eight bit um character sets and uh
3:19doubling the number from 128 to 256
3:22different characters
3:24this enabled um
3:26the
3:27storage and storage of such characters
3:29such as copyright sign
3:31letters with them certain access accents
3:33over them and various uh other european
3:36languages such as the greek alphabet
3:38including
3:39alpha beta
3:41and we have pie and um and omega
3:45but even with that
3:47um it wasn't really enough
3:49so a new system was developed um a
3:51system called unicode
3:54which went from a to 16 to 32-bit um
3:57character sets
3:59um so you can with 32 bits you can
4:02you can still rather rather a lot of
4:05information in fact with 32 bits as you
4:07can see here
4:09we can store over 2 billion different
4:12characters
4:13with his two bits so with this now all
4:16the emojis that you're obviously
4:19familiar with um can be stored as well
4:21and you can see there i've used the
4:22website unicode
4:24table.com
4:26a great website for finding out all
4:28about
4:29um unicode and the various other
4:31character sets that are available
4:36moving on
4:37second part representation of sound
4:41now remember as we said at the very
4:42beginning um computers can only work in
4:45binary
4:46all data must be converted into binary
4:49in order for a computer to process it
4:51sound
4:52is of no exception
4:54to do this sound is captured usually by
4:57a microphone and then converted um into
5:00a digital signal
5:02when we speak when we sing or hear
5:04noises our ears sense vibrations and
5:07interpret them as sound these are what
5:09we know as sound waves
5:11each sound wave has a frequency
5:14as a wavelength and an amplitude the
5:16amplitude specifies the loudness of the
5:19sound
5:20sound waves vary continuously this means
5:23that sound is analog
5:25computers cannot work with analog data
5:27so sound waves need to be sampled in
5:29order to be stored in a computer
5:31sampling means measuring the amplitude
5:34of a sound wave this is done using an
5:37analog to digital converter as you can
5:39see the thing flashing here something
5:41called an adc
5:43analog to digital converter
5:48okay so to convert the analog data to
5:50digital the sound waves are sampled at
5:52regular time intervals the amplitude of
5:55the sound cannot be measured precisely
5:57so approximate values
5:59are stored
6:00what does that mean well if we use this
6:02graph as an example the graph shows a
6:04sound wave being sampled the x-axis the
6:08thing along the bottom shows the time
6:10intervals when the sound was sampled um
6:13in this case 1 to 20 to 21
6:16and the y-axis shows the amplitude of
6:18the of the sample sound up to zero to
6:21ten
6:24um at time interval one you see here the
6:27approximate amplitude is ten at time
6:29interval two the approximate amplitude
6:31is four and so on all 20 time intervals
6:35because the amplitude range shown is 0
6:38to 10 then 4-bit binary can be used to
6:40represent each amplitude value for
6:42example
6:449 will be represented
6:47converted to binary
6:49with the value 1 0 0 1.
6:53so increasing the number of possible
6:54values used to represent sound amplitude
6:57also increases the accuracy of the
6:59sample sound for example using a range
7:02of 0 to 127 gives a much more accurate
7:05representation
7:07of the sound sample
7:09the number of bits per sample is known
7:11as a sample resolution also known as a
7:14bit depth
7:15so in the example on the previous
7:18slide the sampling resolution is four
7:21bits
7:22sampling rate is the number of sound
7:24samples taken per second this and you
7:26may be familiar with this term this is
7:28measured in hertz
7:30where one hertz means one sample per
7:33second
7:35so how is sampling used to record a um a
7:37sound clip well the amplitude of the
7:40sound wave is first determined at set
7:42time intervals the sampling rate
7:45this gives an approximate representation
7:47of the sound wave
7:48and each sample of the sound wave is
7:51then encoded as a series of binary
7:53digits
7:55of course using a
7:56higher sampling rate or larger
7:58resolution will result in a more
8:00faithful faithful representation of the
8:03original sound source however the higher
8:06the sampling rate and or sampling
8:08resolution the greater the file size
8:12so the benefit there are obviously
8:14benefits and drawbacks of using a larger
8:16sampling resolution when recording sound
8:18the benefits obviously um
8:21much
8:22better sound quality larger dynamic
8:24range and um less sound distortion
8:28but
8:29of course it's going to produce larger
8:30file sizes
8:32these large files
8:33are going to take longer to transmit or
8:35download
8:37over the internet
8:39and it potentially would require greater
8:41processing power
8:43now there is a standard um used
8:46generally mp3s and cds and they are
8:49sampled um using 16-bit sampling
8:51resolutions and a 44.1 kilohertz
8:55sampling rate
8:56that is 44 100 samples every second
9:00this being standard gives a very high
9:02quality sound reproduction
9:06and finally how do we represent bitmap
9:10images
9:11um on the computer
9:13um well bim up images are made up of
9:15pixels a term you may be familiar with
9:18and pixels is short for picture elements
9:22an image stops installing your computer
9:24is made up of a two-dimensional matrix
9:27of pixels and pixels can take
9:30different shapes such as side by side in
9:32this case under circles um
9:35join together as so
9:37so there are pixels in all images that
9:39are pixels on your iphone um on your tv
9:42screen on your computer screen
9:45the more pixels in an image the closer
9:47you get to the original image
9:51um each pixel
9:54each little tiny square can be
9:56represented as a binary number and so a
9:59bitmap image is stored in a computer as
10:01a series of binary numbers so that in
10:04the example below a black and white
10:06image only requires
10:08one bit per pixel
10:10to store its information this means that
10:12each pixel
10:14can be one of two colors corresponding
10:17to either a one
10:18or a zero
10:20so this little black and white face
10:22which we've got on here um an eight by
10:26eight pixel grid black and white image
10:28uses 64 bits to store the bitmap only
10:32zeros and ones
10:37now if we want to increase the colors
10:40um i'm showing an example here i've
10:43increased it from a one bit two colors
10:46to a two bit image
10:49um
10:50if each pixel is represented by two bits
10:52then each pixel can be one of four
10:55colors so two to the two equals four
10:59corresponding to the value zero zero
11:01zero one
11:02one zero or one one
11:05okay
11:06obviously this doubles every time so if
11:09each pixel is represented by three bits
11:12then each pixel can be one of eight
11:14colors
11:15two to the three equals eight different
11:17colors as you can see there they would
11:19be the corresponding numbers
11:22the number of bits used to represent
11:23each color is called the color depth an
11:278-bit color depth means that each pixel
11:29can be one of 256
11:32different colors
11:34modern computers your macbooks
11:37or pc laptops
11:39modern computers have a 24-bit color
11:42depth which means over 60 million
11:44different colors can be represented
11:47represented
11:49with x pixels 2 to the x colors
11:52can be represented as a generalization
11:54increasing the color depth also
11:56increases the size of the file when
11:58storing an image basically like with
12:01sound the better the quality
12:03the the
12:04bigger the file you're gonna need to
12:06store
12:09image resolution refers to the number of
12:10pixels that make up an image for example
12:13an image could contain 4096 by 3072
12:17pixels so we basically multiplied those
12:19two together the sort of the grid matrix
12:22and we would in this case end up with 12
12:24over 12 million
12:26um
12:27different picks 12 million pixels
12:29with each one being
12:31a potentially different color
12:33three
12:34um
12:36sorry five different pictures or five
12:39five images all of the same car wheel
12:43image a has the highest resolution the
12:46most number of pixels
12:48um and imagee is a lower quality and far
12:51fewer pixels
12:53image e has become pixelated or fuzzy
12:57um this is a result of changing the
12:59number of pixels per centimeter used to
13:01store the image
13:03that is it's reducing um the picture
13:05resolution
13:09so there are drawbacks the main drawback
13:11of using high resolution images
13:13live with sound is it increases the file
13:16size
13:17as the number of pixels used to
13:18represent the image is increased the
13:20size of the file will also increase
13:23this impacts on how many images can be
13:26stored on for example hard drive on your
13:28computer it also impacts on the time to
13:30download an image from the internet all
13:32the time to transfer the image from one
13:34device to another
13:36a certain amount of reduction in
13:37resolution of an image is possible
13:40before the loss of the quality can or
13:42will become noticeable
13:46now that is it for this next part we
13:50will be moving on into in the next video
13:52into more detail about file compressions
13:55and how we can look at lossy and lost
13:57lossless compression to come to compress
14:00these files before we send them on
14:03to other computers
14:05but
14:06for now i'd like to thank you very much
14:07for watching and thank you so much for
14:09subscribing to this channel and i will
14:11see you in the next video thank you very
14:14much indeed bye for now

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