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Intro to IMD Final > Unit 04 - Compression > Flashcards

Unit 04 - Compression Flashcards

1
Q

Why does compression matter?

A

People generate a ton of data.

A very good digital photo = about 12 MegaPixels (MP)

12 MP x 3 bytes per pixel = 36 MB

… which is already a lot

But with video, we have to multiply each frame size by 24 or 30 frames per second

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2
Q

So why does compression matter?

A

Basically, compression enables us to store data more efficiently

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3
Q

What are some common media types?

A

Compression is widely used across media types.

Some common ones:

Images: jpeg, png, gif

Videos: mpeg 1, 2, 4, 7 (21)

Audio: mp3, m4a

Data files: rar, zip (lzw)

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4
Q

What are some fundamental principles about compression?

A

Compression relies on both spatial coherence and temporal coherence.

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5
Q

What is spatial coherence?

A

Similarity with neighbour across space

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6
Q

What is temporal coherence?

A

Similarity with neighbour over time

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7
Q

Spatial

A

Our visual system is more sensitive to differences in light than colour, so we need to store less chroma information than luminance.

Chroma: intensity of colour
Luminance: intensity of light

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8
Q

Spatial continued

A

Spatial coherence reduces redundancy through chroma subsampling.

Divide the image into macroblocks to reduce file size.

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9
Q

Spatial continued. What are consequences of high compression?

A

Consequence of high
compression:
Compression artifacts
(noticeably distorted)

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10
Q

Temporal is also known as?

A

Inter-frame

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11
Q

Temporal

A

AKA inter-frame
Reduce redundancy between frames, which often have lots in common.

Instead of storing data for every pixel, each macroblock gets instructions on how to change from their current state using keyframes.

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12
Q

Temporal continued. What is a keyframe?

A

Keyframe: Defines the starting and ending points of a smooth transition

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13
Q

Lossless vs lossy?

A

Lossless compression: output data is identical to input data

Lossy compression: output data is not identical to input data
-Acceptable for data that is only viewed or heard

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14
Q

What is lossless commonly used for?

A

Lossless commonly used for:
Sensitive documents
Confidential info
PNG, RAW, GIF, BMP files

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15
Q

What is lossy commonly used for?

A

JPEG
MPEG, MP3

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16
Q

Higher compression = ____ files = ___ loss

A

Higher compression = smaller files = more loss

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17
Q

Lower compression = ____ files = ____ loss

A

Lower compression = bigger files = less loss

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18
Q

Encoding. Vocabulary

A

Character: a basic data unit in the input stream that forms words and phrases (e.g. English latin letter a, Chinese ideograph 請)

Strings: sequences of characters

char letter = ‘a’;
string word = ‘apple’;

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19
Q

Another word for encoding?

A

Compression

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20
Q

Another word for decoding?

A

Decompression

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21
Q

What is codeword?

A

Data elements used to represent input characters or character strings

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22
Q

Codetable

A

Codetable: stores the mapping between display value and stored (data) values

Codetables are like dictionaries for encoded messages

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23
Q

Compression ratio

A

Compression ratio: the relative reduction in size produced by a data compression algorithm
(higher = better)

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24
Q

How do you calculate the compression ratio?

A

Compression ratio = uncompressed size / compressed size

Example - mp3: 32-megabyte/3-megabyte = 10.66 or 10:1

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25
Q

What is the compression process?

A

The encoder takes strings as input and uses a codetable to decide on which codewords to produce

Input Data Stream -> Encoder -> Data storage or transmission -> Decoder -> Output data stream

The decoder takes codewords as input and uses the same codetable to decide on which characters to produce to recreate the string

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26
Q

Codetables

A

Encoder and decoder pass the encoded data as a series of codewords, along with the codetable.

The codetable can be passed explicitly or implicitly, by:

-sending it across
-agreeing on it beforehand (hard wired)
-recreate it from the codewords (clever!)

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27
Q

Symmetry

A

Symmetric compression:
time to encode = time to decode

Asymmetric compression:
time to encode < decoding

Symmetric algorithms are consequently often used for live activities, like streaming media

Asymmetrical algorithms are useful for backing up or exporting.

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28
Q

What is Entropy Encoding?

A

The measurement of the entropy of a thing tells you how much information you need to describe it.
Example:

You can describe hhhhhhhh with just 9*h – it has low entropy

The string arghbdqwplsk!? requires
more information to describe – it has higher entropy

Entropy encoding is lossless &
independent of the specific
characteristics of the medium being
compressed.

Common examples of entropy
encoding:
● Run-length
● Huffman
● Lempel Ziv Welch

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29
Q

Run-Length Encoding (RLE)

A

Run-length encoding (RLE) is a simple early & simple compression method (used by MacPaint & fax machines)

● Leverages repeating data
(called runs)
● Data are represented by a count of the run length along with the original data (e.g. AAAABB => 4A2B)

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30
Q

What are the steps for run-length encoding?

A
  1. Count until there is a change in data
  2. Then start the next count
  3. Repeat - while moving cell-by-cell: left-right, top-bottom
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31
Q

Run-length encoding

A

Run-length encoding is lossless and has fixed-length codewords

-Best for images with solid backgrounds (e.g. cartoons)

-Less effective for natural images (e.g. photos) and English text, where runs are short

Run-length encoding is almost always a part of a larger compression system, like JPEG

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32
Q

Huffman encoding

A

While run-length codewords are fixed length, Huffman codewords are variable length

● Length of codewords is inversely
proportional to frequency
● In variable length compression one code cannot be the prefix of another
● A binary tree structure guarantees this

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33
Q

How to do Huffman coding

A

There are two major parts in Huffman Encoding

  1. Build a Huffman Tree from input characters
  2. Traverse the Huffman Tree and assign codes to characters
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34
Q

Huffman coding Example

A

I’m going to encode the string: this is my example

● 18 characters
● 1 character = 8 bits
● 18 characters * 8 bits = 144 bits
● My string is 144 bits before compression
● Let’s see if we can improve on that!
● Characters include: letters, punctuation, spaces

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35
Q

Huffman coding example continued

A
  1. Build a tree

A. Create a leaf node for each unique character (*case
sensitive) and build a min heap (a list) of all leaf nodes.

○ I’ll be attempting to encode the 18-character phrase:
this is my example

○ Go through your encoding string and add a row every time you
encounter a unique character

○ Add a tick mark beside it every time you encounter it (including
the first)

○ Sum up the ticks for each row and write that number in a new
column (Count)

Char Freq Count
t | 1
h | 1
i || 2
s || 2
_ ||| 3
m || 2
y | 1
e || 2
x | 1
a | 1
p | 1
l | 1

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36
Q

Huffman coding example continued

A
  1. Build a tree

A. …
○ Add up ALL the counts & make sure that total = the number
of characters in your string.

○ Sort the table by the count column (descending), so your
table has most frequent characters at the top.

Char Freq Count
_ ||| 3
i || 2
s || 2
m || 2
e || 2
t | 1
h | 1
y | 1
x | 1
a | 1
p | 1
l | 1

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37
Q

Huffman coding example continued

A
  1. Build a tree

B. Take the bottom two characters (the ones with the minimum frequencies) out of your list.

○ In my example:
■ p (frequency: 1)
■ l (frequency: 1)

Char Count
_ 3
i 2
s 2
m 2
e 2
t 1
h 1
y 1
x 1
a 1
p 1
l 1

38
Q

Huffman coding example continued

A
  1. Build a tree

C. Create a new node with a frequency equal to the sum of the two nodes frequencies.

Note the order you put the nodes. If you keep them consistent, it will help you with decoding later.

Add this new node back to the list (wherever it belongs in order).

pl (2)
p1 l1

_3 i2 s2 m2 e2
t1 h1 y1 x1 a1

Char Count
_ 3
pl 2
i 2
s 2
m 2
e 2
t 1
h 1
y 1
x 1
a 1
p 1
l 1

39
Q

Huffman coding example continued

A
  1. Build a tree

D. Then repeat this process until you run out of nodes

For anything other than super short strings, it can take a while

_3 i2 s2 m2 e2
t1 h1 y1

pl (2) ax (2)
p1 l1 a1 x1

Char Count
_ 3
ax 2
pl 2
i 2
s 2
m 2
e 2
t 1
h 1
y 1
x 1
a 1
p 1
l 1

40
Q

Huffman coding example continued

A
  1. Build a tree

_3 i2 s2 m2 e2
t1

pl (2) ax (2) hy (2)
p1 l1 a1 x1 h1 y1

Char Count
_ 3
hy 2
ax 2
pl 2
i 2
s 2
m 2
e 2
t 1
h 1
y 1
x 1
a 1
p 1
l 1

41
Q

Huffman coding example continued

A
  1. Build a tree

_3 i2 s2 m2

pl (2) ax (2) hy(2) et (3)
p1 l1 a1 x1 h1 y1 e2 t1

Char Count
et 3
_ 3
hy 2
ax 2
pl 2
i 2
s 2
m 2
e 2
t 1
h 1
y 1
x 1
a 1
p 1
l 1

42
Q

Huffman coding example continued

A
  1. Build a tree
    _3 i2

pl (2) ax (2) hy(2) et(3) sm (4)
p1 l1 a1 x1 h1 y1 e2 t1 s2 m4

Char Count
sm 4
et 3
_ 3
hy 2
ax 2
pl 2
i 2
s 2
m 2
e 2
t 1
h 1
y 1
x 1
a 1
p 1
l 1

43
Q

Huffman coding example continued

A
  1. Build a tree
    pli(4)
    sm(4) et(3) hy(2) ax(2) pl(2)
    s2 m2 e2 t1 h1 y1 a1 x1 p1 l1 i2

Char Count
pli 4
sm 4
et 3
_ 3
hy 2
ax 2
pl 2
i 2
s 2
m 2
e 2
t 1
h 1
y 1
x 1
a 1
p 1
l 1

44
Q

Huffman coding example continued

A
  1. Build a tree
                             hyax(4)          pli(4) sm (4)    et(3)    hy(2)    ax(2)    pl(2)  s2 m2    e2 t1   h1 y1   a1 x1   p1 l1 i2

Char Count
hyax 4
pli 4
sm 4
et 3
_ 3
hy 2
ax 2
pl 2
i 2
s 2
m 2
e 2
t 1
h 1
y 1
x 1
a 1

45
Q

Huffman coding example continued

A
  1. Build a tree

et_(6) hyax(4) pli(4)
et(3) sm(4) hy(2) ax(2) pl(2)
e2 t1 _3 s2 m2 h1 y1 a1 x1 p1 l1 i2

Char Count
et_ 6
hyax 4
pli 4
sm 4
et 3
_ 3
hy 2
ax 2
pl 2
i 2
s 2
m 2
e 2
t 1
h 1
y 1
x 1

46
Q

Huffman coding example continued

A
  1. Build a tree
    smpli (8)
    et_ (6) hyax(4) pli(4)
    et(3) sm(4) hy(2) ax(2) pl(2)
    e2 t1 _3 s2 m2 h1y1 a1x1 p1 l1 i2

Char Count
smpli 8
et_ 6
hyax 4
pli 4
sm 4
et 3
_ 3
hy 2
ax 2
pl 2
i 2
s 2
m 2
e 2
t 1
h 1
y 1

47
Q

Huffman coding example continued

A
  1. Build a tree

et_hyax (10) smpli(8)
et_(6) hyax(4) pli(4)
et(3) hy(2)ax(2) sm(4) pl(2)
e2t1_3 h1y1 a1x1 s2m2 p1l1 i2

Char Count
et_hyax 10
smpli 8
et_ 6
hyax 4
pli 4
sm 4
et 3
_ 3
hy 2
ax 2
pl 2
i 2
s 2
m 2
e 2
t 1
h 1

48
Q

Huffman coding example continued

A
  1. Build a tree
    et_hyaxsmpli (18)
    0 1
    et_hyax (1) smpli(8)
    et_(6) hyax(4) pli(4)
    et(3) hy(2)ax(2) sm(4)pl(2)
    e2t1_3 h1y1 a1x1 s2m2p1l1i2

Char Count
et_hyax 10
smpli 8
et_ 6
hyax 4
pli 4
sm 4
et 3
_ 3
hy 2
ax 2
pl 2
i 2
s 2
m 2
e 2
t 1
h 1

49
Q

Huffman coding example continued

A
  1. Traverse the Huffman Tree and assign codes to characters

so at the top is

et_hyaxsmpli (18)
to the left down would be 0
to the right down would be 1

it follows this pattern

so left is 0 and to the right is 1

at the bottom you get your code

so for example, if you go all the way down the left and keep going left you would get 0000

the char _ would be 001

i would be 111 because i at the bottom is all the way at the bottom right

Do this for all the characters and you get

Char Count Code
_ 3 001
i 2 111
s 2 100
m 2 101
e 2 0000
t 1 0001
h 1 0100
y 1 0101
x 1 0111
a 1 0110
p 1 1100
l 1 1101

50
Q

So what is the compressed size after doing all the huffman coding?

A

Recall that the original string was 144 bits uncompressed because there was 18 characters x 8 bits because 1 character = 8 bits

18 characters * 8 bits = 144 bits before compression

To calculate the size of the compressed string:
1. Calculate the number of bits per code
2. Multiply the number of bits by the count for each character
3. Add them all up (count size)
=63 bits when you add up all of the count size

Char Count Code Size (bits) Count * Size
_ 3 001 3 9
i 2 111 3 6
s 2 100 3 6
m 2 101 3 6
e 2 0000 4 8
t 1 0001 4 4
h 1 0100 4 4
y 1 0101 4 4
x 1 0111 4 4
a 1 0110 4 4
p 1 1100 4 4
l 1 1101 4 4

Just multiply the bits by the count to get the count size

So 3 count * 3 size (bits) = 9 count size and then add up all the count sizes to get the size of the compressed string which is 63 bits

51
Q

How do you calculate the compression ratio?

A

Compression ratio = uncompressed size/compressed size

Compression Ratio = 144/63

Compression Ratio = 2.3

52
Q

Huffman Decoding

A

Browse the tree from root to leaves (top to bottom) until you reach an existing leaf. This is the decoded character

000101001111000011111000011010101001000001110110101110011010000

this is my example

53
Q

What are the advantages to using Huffman?

A

Assuming the correct probabilities (or frequencies), Huffman coding is optimal for encoding single characters
(Relatively) quick and easy to implement

54
Q

What are the disadvantages to using Huffman?

A

Not optimal for encoding multiple characters with a single encoding

Requires two passes for both encoding and decoding

Avoid this by sending the tree (takes time) or using consistent frequencies (less efficient)

55
Q

How do you calculate the compressed size when doing huffman decoding?

A
  1. Calculate the number of bits per code, meaning if the code is 001 the number or size of bits is 3. If the code is 0000 then the number or size of bits is 4.
  2. Multiply the number of bits by the count for each character

The count just means how many times this character appears in the string. For example, i appears 2 times in the phrase: “This is my example” so that means i has a count of 2.

So you multiply the number of bits, for i in this case the number of bits is 3 because the code for i is 111.

You multiply 3 by the size of bits which is 3 because of the code 001.

so 3 x 3 = 9 and that’s your count size. You then do this to each character that appears in the string.

  1. Add them all up (Count size) so 9+6+6+6+8+4+4+4+4+4+4+4 = 63 bits

based on our example “This is my example”

56
Q

How do you calculate compression ratio?

A

You take the uncompressed size which is the number of characters times 8 bits because 1 character is 8 bits. So “This is my example” has 18 characters in it. 18 * 8 = 144 bits

This is your uncompressed size

You then take your compressed size which is 63 bits after adding up all the count sizes which you get by multiplying the count (how many times the character appears in the string) by their size(bits) (determined by code, so 000 = 3, 0000 = 4)

You divide the uncompressed size by the compressed size so:

144/63 = 2.3

57
Q

Lempel-Ziv-Welch LZW

A

Lempel Ziv Welch (LZW)
● A lossless compression algorithm
● Previous methods worked only on characters,
but LZW works by encoding strings
● This makes it possible to encode some longer
strings with a single codeword

58
Q

Lempel-Ziv-Welch LZW continued

A

The string to codeword mappings are created
dynamically by the encoder
● … and also recreated dynamically by the decoder,
so there’s no need to pass the codetable between
the two

59
Q

LZW steps

A
  1. Create a dictionary with all the possible input characters
  2. Scan through the input string for successively longer
    substrings until you find one that is not yet in the
    dictionary
  3. The index for the string without the last character
    (longest substring that
    is in the dictionary) is retrieved
    from the dictionary and sent to output
  4. The new string (including the last character) is added
    to the dictionary with the next available code
  5. The next input character in your string is then used as
    the starting point to scan for substrings, repeating
    Steps 2–5 until complete
60
Q

LZW: Example

A

Now, let’s suppose our input stream
we wish to compress is:
banana_bandana
Create a dictionary with all the
possible input characters
The individual characters are the first
entries.
Index Entry
0 a
1 b
2 d
3 n
4 _

61
Q

LZW: Example continued

A

Scan through the input string for successively
longer substrings until you find one that is not
yet in the dictionary.
The longest substring that
is in the dictionary is
retrieved from the dictionary and sent to
output
Recall that our string is: banana_bandana
And b is already in the dictionary, but ba is not
(yet!)
Index Entry
0 a
1 b
2 d
3 n
4 _
Encoded output:

62
Q

LZW: Example continued

A

banana_bandana
The next string that isn’t yet in the
dictionary is: ba
Add that to the dictionary at the next
available index (just count up).
Index Entry
0 a
1 b
2 d
3 n
4 _
5 ba
Encoded output: 1

63
Q

LZW: Example continued

A

Step 2. Scan through the input string for
successively longer substrings until you
find one that is not yet in the dictionary.
Recall that our string is:
banana_bandana
The next string that isn’t yet in the
dictionary is: an
Index Entry
0 a
1 b
2 d
3 n
4 _
5 ba
6 an
Encoded output: 1,0

64
Q

LZW: Example continued

A

Step 2. Scan through the input string for
successively longer substrings until you
find one that is not yet in the dictionary.
Recall that our string is:
banana_bandana
The next string that isn’t yet in the
dictionary is: na
Index Entry
0 a
1 b
2 d
3 n
4 _
5 ba
6 an
7 na
Encoded output: 1,0,3

65
Q

LZW: Example continued

A

Step 2. Scan through the input string for
successively longer substrings until you
find one that is not yet in the dictionary.
Recall that our string is:
banana_bandana
The next string is: an … but that already
is in the dictionary (entry 6!)
No new entries. But next step, we’ll
have to add 6 to the encoded output.
Index Entry
0 a
1 b
2 d
3 n
4 _
5 ba
6 an
7 na
Encoded output: 1,0,3

66
Q

LZW: Example continued

A

Step 2. Scan through the input string for
successively longer substrings until you
find one that is not yet in the dictionary.
Recall that our string is:
banana_bandana
So move to the next string that is not
yet in the dictionary: ana
Remember to add Index 6: an to our
encoded output.
Index Entry
0 a
1 b
2 d
3 n
4 _
5 ba
6 an
7 na
8 ana
Encoded output: 1,0,3,6

67
Q

LZW advantages

A

Adapts to the content
● Simple and fast
● Good compression
● Compresses in a single pass: A
dynamic codetable built for each file,
but the decoder recreates the
codetable, so it does not need to be
passed

68
Q

LZW disadvantages

A

Not the optimum compression ratio
● Actual compression hard to predict
● Compression doesn’t typically start until a large number of bytes (> 100) are read in

69
Q

LZW use

A

Used in ZIP and GIF encoding and
decoding applications
● Patent expired mid 2003 for the US,
mid 2004 for the rest of the world
● IBM’s own variant patent expired
Aug 11, 2006
● Open source GIF encoders &
decoders are now legal!

70
Q

Entropy encoding. Entropy in action

A

RLE, Huffman, and LZW are all lossless and
entropy based
● Lossless methods are essential for computer
data (zip, gnuzip, etc)
● Run-length encoding + Huffman encoding is a
standard combined tool
● They are often used as a subroutine by other
lossy methods (JPEG, MPEG)

71
Q

Source encoding

A

Source encoding takes into account type of data (e.g.
visual). It is typically lossy, but can be lossless.
Common types of source encoding:
● JPEG, GIF (images)
● MPEG (video)
● MP3 (audio)
Source encoding often
uses entropy encoding as a
sub-routine.

72
Q

Is source encoding lossy or lossless?

A

Source encoding is typically lossy, but can be lossless

73
Q

Common types of source-based compression

A
  1. Transform methods
    ● Used for “natural” data like audio
    signals or photos
    ● Knowledge of the application is
    used to choose information to
    discard, thereby lowering its
    bandwidth
    ● Remaining information is then
    compressed via various methods
  2. Predictor methods
    ● For sequences
    ● Leverage temporal coherence to
    store only the difference between
    the actual value and prediction of
    that value
    ● Simplest possible prediction is
    just to use the previous value
74
Q

JPEG

A

JPEG is not a file format, it’s a lossy compression method

Published in 1992

Supports a maximum image size of 65,535 x 65,535 pixels, up to 4 gigapixels for an aspect ratio of 1:1

75
Q

how to jpeg

A

How to JPEG
1. Partition image into 8 by 8 picture macroblocks
2. Discrete cosine transform (DCT) each block
(going from left to right)
3. Remove high-frequency data by quantizing the
DCT coefficients
4. Entropy code the reduced coefficients
5. Display the image by reconstructing it through
decompression

76
Q

Discrete cosine transform (DCT)

A

discrete cosine transform (DCT)

a process that compresses images

separates an image into its luminance and chrominance

These spectral sub-bands are given differing importance, by mapping them onto cosine waves to generate DCT coefficients

77
Q

Quantizing

A

The loss-producing (lossy) step

Compresses a range of DDCT coefficients into a single value

Each DCT coefficient is divided by a quantization value and rounded

A standard quantization table is 8 by 8

Larger values (more loss) at higher frequencies

78
Q

Entropy encoding for JPEG

A
  1. Take quantized DCT coefficients and store them together as a sequence of integers
  2. Perform run-length encoding (ordered as a zig-zag to maximize the chance of a run)
  3. Do Huffman encoding on the runs - this is a lossless step

Compression ratios of 20:1 are common!

79
Q

JPEG vs PNG

A

JPEG is lossy

JPEG is better for storing photographs and realistic images.

PNG is lossless

Better for storing line drawings, text, and iconic graphics.

Both are common choices for use on the web.

80
Q

GIF

A

Short for Graphics Interchange Format

Pronounced with a hard G, like guppy or good

Palette of up to 256 different colours

Relies on Lempel-Ziv-Welch (LZW) lossless data compression method

81
Q

What is bitrate?

A

Bitrate is the number of bits per second that can be transmitted along a digital network. Higher bitrate, more data, higher quality video.

But! Recall

Higher compression = smaller files = more loss

lower compression = bigger files = less loss

82
Q

Video compression picture types

A

I-frame (Intra-coded): a full image, like a JPG or BMP image file.

P-frame (Predicted): holds only the changes in the image from the previous frame. Also known as delta-frames.

B-frame (Bidirectional predicted): even more efficient by predicting content, using differences between the current frame and the frames that are before and after it.

83
Q

Moving Picture Experts Group (MPEG)

A

So many standards!

The full set of MPEG standards is long and includes a lot of different types of specification.

Full knowledge of them all is NOT required for this course.

84
Q

MPEG-1

A

Released in 1993

Intended for storage of synchronized audio and video signals on CD

Optimized for NTSC TV, with 324 x 240 pixel resolution, same frame rate as broadcast TV (~29.97 fps)

Non-interlaced (progressive scanning)

Typical compression is 26:1

85
Q

MPEG-2

A

Designed for studio quality motion video

Handles a resolution of 720 x 480

Both progressive and interlaced scan modes

Actually emerged as a standard for HDTV (High definition television)

86
Q

MPEG-3

A

Designed to handle HDTV

It became unnecessary because MPEG-2 was found able to accommodate HDTV

87
Q

MPEG-4

A

MPEG-4 absorbed and improved on many of the features of MPEG-1 and MPEG-2 and other related standards

Ability to encode mixed media data (video, audio, speech)

Error resilience for robust transmission

Initially intended for low bit-rate video communications

Expanded to be efficient across a variety of bitrates (from several kbps to tens of mbps)

88
Q

MPEG-4: Compression

A

Uses predictor method for video sequences

Basically JPEG with some prediction, also called motion compensation

Assuming a frame rate of 30fps: Two frames in a row do not vary a lot in 1/30th of a second unless the camera is moving very fast!

89
Q

H.264

A

MPEG-4 Part 10, Advanced Video Coding (AVC)

A more advanced compression method than MPEG-4 & very popular

Higher compression rate (1.5-2x more efficient) than MPEG-4 ratio of about 2000:1

Better image quality

More fluent playback

lower bitrate required for network transmission

Now part of MPEG-4 (part 10 aka AVC)

90
Q

MP4

A

MPEG-4 Part 14

Introduced in 2001

A digital multimedia container format most commonly used to store video and audio

Can also store other data (subtitles, images)

Supports streaming

Most digital platforms and devices support MP4

Intro to IMD Final (4 decks)

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