info 101 ch 8
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Chapter 8
Representing Multimedia Digitally
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Learning Objectives
Explain how RGB color is represented in bytes Explain the difference between bits and binary
numbers
Change an RGB color by binary addition Explain concepts related to digitizing sound waves Explain the meaning of the Bias-Free Universal Medium
Principle
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Digitizing Data
Digitizing is more than letters, numbers,and metadata
It is also photos, audio, and video What are the bits doing?
Digitizing includes other forms of digitizedinformation, known as multimedia
Same principles are used as with lettersand numbers to encode informationinto bits
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Color and the Mystery of Light
Color on a Computer Display: Pixels are small points of colored light
arranged in a Each pixel is formed from three colored lights:
red, green, and blue. Referred to as RGB (always in that order)
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Showing Colors
Turning on one light at a time, the displayturns red, green, or blue
Turning off all of them makes black Turning on all of them makes white All other colors are made by using
different amounts or intensities of the threelights The three colors do not have to be
RGB
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Yellow = R + G?
Combining red andgreen makes yellow
Taught in elementary
school that red andyellow are primarycolors
There is a differencebetween colored lightand colored paint
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Yellow = R + G?
Paint reflects somecolors and absorbsothers
When white lightstrikes paint, somelight is absorbed (wecant see it) and somelight is reflected (wesee it)
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Yellow = R + G?
In the case of a pixel,the light shinesdirectly at our eyes
Nothing is absorbed Nothing is reflected Just see pure colored
light
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LCD Display Technology
At left in the close-upof an LCD is an arrowpointer with two
enlargements of it From a distance, the
pixels appear white Close up, the pixels
are red, green, andblue colored lights
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Black and White Colors
The intensity of RGB light is usually givenby a binary number stored in a byte
Representing the color of a single pixelrequires 3 bytes (1 for each color) Smallest intensity is 0000 0000 Largest value is 1111 1111
Doing the math from Chapter 7, says therange of values is 0 through 255 foreach color
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Black and White Colors
Black is the absence of light: 0000 0000 0000 0000 0000 0000
RGB bit assignment for black
White is the full intensity of each color: 1111 1111 1111 1111 1111 1111
RGB bit assignment for white
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Color Intensities
Consider blue (0000 0000 0000 0000 1111 1111 ) The 8 bits specifying its intensity have
position values:
If we want the sub pixel to at half intensity:
each bit contributes half as much
power as the bit to its left
128 64 32 16 8 4 2 1
1 1 1 1 1 1 1 1
128 64 32 16 8 4 2 1
1 0 0 0 0 0 0 0
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Color Intensities
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Decimal to Binary
Which powers of 2 combine to make thedecimal number?
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Lighten Up
Changing Colors by Addition To make a lighter color of gray , we change
the common value to be closer to white.
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Lighter Still
Imagine that the color lighter still byanother 16 units of intensity for each RGBbyte
The 16s position is already filled with a 1:1101 1110
Carry to the next higher place
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Binary Addition
Same as decimal addition but with onlytwo digits
Work from right to left, adding digits ineach place position, writing the sum below
Like decimal addition, there are two cases: Add the two numbers in a place and the result
is expressed as a single digit Add two numbers in a place and the result
requires carrying to the next higher place
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Computing on Representations
When digital information is changedthrough computation, this is computing onrepresentations.
For example: changingthe brightness andcontrast of a photo
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Brightness and Contrast
Brightness refers to how close to white thepixels are
Contrast is the size of difference betweenthe darkest and lightest portions of theimage.
Photo manipulation software often givesthe values of the pixels in a Levels graph
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Levels Graph
0 percent is called theblack point, or 000000
100 percent is the
white point, or ffffff The midpoint is called
the gamma point andit is the midpoint inthe pixel range
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Brightness
We want all the pixelsto be nearer intensewhite, but to keep their
relative relationships Add 16 to each pixel A pixel which is
197, 197, 197 becomes213, 213, 213
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Contrast
Goal is not to shift theLevels diagram right,but rather to stretch it
out toward the right Add an amount to each
pixel as before add a smaller amount for
dark pixels Add a larger amount for
light pixels
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New Levels Graph
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New Levels Math
For every original pixel P o, subtract theamount of the lower end of the range:P o 38
That tells how much to increase each pixelposition; smaller (darker) numbers getlightened less than larger (lighter)numbers
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New Levels Math
Then we multiply by the size of the newinterval divided by the size of the oldinterval
Add the low end of the original range backin again to return each pixel to its newposition along the second line
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New Levels Math
The equation for the value in each pixelposition of the new image:P n = (P o 38)*1.08 + 38
Round the answer to a whole number
Try it yourself!For original pixel 239, did you get 255?For original pixel 157, did you get 167?
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Adding Color
Whenever the 3 bytes differ in value thereis color
Define highlights as the lightest 25percent of the pixels, and shadows asthe darkest 25 percent of the pixels
Must count the pixels to know thosevalues: There are 600 800 = 480,000 pixels
in the image
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Adding Color
Pick the lowest pixel value and go up tothe next level and keep adding until youhave approximately of the total pixels (inthis case 120,000)
Pick the highest pixel value and go downto the next level, adding until you have thetop of the total pixels
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Adding Color
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Digitizing Sound
An object creates sound by vibrating in amedium (such as air)
Vibrations push the air causing pressurewaves to emanate from the object, whichin turn vibrate our eardrums
Vibrations are then transmitted by threetiny bones to the fine hairs of our cochlea,stimulating nerves that allow us to sensethe waves and hear them as sound
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Digitizing Sound
The force, or intensityof the push,determines the
volume The frequency (the
number of waves persecond) of the pushesis the pitch
continuous (analog)representation of the wave
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Analog to Digital
To digitize you must convert to bits For a sound wave, use a binary number to
record the amount that the wave is aboveor below the 0 line at a given point on ourgraph
At what point do you measure? There are infinitely many points along
the line, too many to record everyposition of the wave
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Analog to Digital
Sample or takemeasurements atregular intervals
Number of samples ina second is called thesampling rate
The faster the rate themore accurately thewave is recorded
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Nyquist Rule for Sampling
If the sampling were too slow, soundwaves could fit between the samples andyou would miss important segments of thesound
The Nyquist rule says that a sampling ratemust be at least twice as fast as thefastest frequency
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Nyquist Rule for Sampling
Because humans can hear sound up toroughly 20,000 Hz, a 40,000 Hz samplingrate fulfills the Nyquist rule for digital audiorecording
For technical reasons a somewhat faster-than-two-times sampling rate was chosenfor digital audio (44,100 Hz)
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Digitizing Process
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Digitizing Process
The digitizing process works as follows: Sound is picked up by a microphone
(transducer ).
Signal is fed into an analog-to-digitalconverter (ADC), which takes the continuouswave and samples it at regular intervals,outputting for each sample binary numbers tobe written to memory.
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Digitizing Process
The digitizing process works as follows: The process is reversed to play the sound: The
numbers are read from memory into a digital-to-analog converter (DAC)
Electrical wave created by interpolation between thedigital values (filling in or smoothly moving from onevalue to another)
The electrical signal is then input to a speaker whichconverts it into a sound wave
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How Many Bits per Sample?
To make the samples perfectly accurate,you need an unlimited number of bits foreach sample
Bits must represent both positive andnegative values Wave has both positive and negative sound
pressure The more bits there that are used, the
more accurate the measurement is
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How Many Bits per Sample?
We can only get anapproximatemeasurement
If another bit is used, thesample would be twice asaccurate
More bits yields a moreaccurate digitization
Audio digitalrepresentation uses 16bits
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Advantages of Digital Sound
A key advantage of digital information isthe ability to compute on therepresentation
One computation of value is to compressthe digital audio or reduce the number ofbits needed
What about sounds that the human earcant hear because they are either toohigh or too low
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Advantages of Digital Sound
MP3 is really a form of computing on therepresentation.
It allows for compression (with a ratio ofmore than 10:1)
Another key advantage of digitalrepresentations is that digital can bereproduced exactly
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Digital Images and Video
An image is a long sequence of RGBpixels
The picture is two dimensional, but think ofthe pixels stretched out one row afteranother in memory
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Digital Images and Video
Example: 8 10 image scanned at 300 pixels per inch Thats 80 square inches, each requiring 300
300 = 90,000 pixels (or 7.2 megapixels) At 3 bytes per pixel, it takes 21.6 MB (3 * 7.2)
of memory to store one 8 10 color image
Sending a picture across a standard 56 Kb/sphone connection would take at least21,600,000 8/56,000 = 3,085 seconds(or more than 51 minutes)
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Image Compression
Typical monitor has fewer than 100 pixelsper inch (ppi) storing the picture digitized at 100 ppi is a
factor of nine savings immediately. A 100 ppi picture still requires more than
five and a half minutes to send
What if we want to print the picture,requiring the resolution again?
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Image Compression
Compression means to change therepresentation in order to use fewer bits tostore or transmit information Example: faxes are a sequences of 0s and
1s that encode where the page is white (0) orblack (1)
Use run-length encoding to specify how longthe first sequence (run) of 0s is, then howlong the next sequence of 1s is, then howlong the next sequence of 0s is, then
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Compression
Run- length encoding is losslesscompression scheme The original representation of 0s and 1s can
be perfectly reconstructed from thecompressed version
The opposite of lossless compression is
lossy compression The original representation cannot be exactly
reconstructed from the compressed form
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Compression
MP-3 is probably the most famouscompression scheme MP3 is lossy because the high notes cannot
be recovered JPG (or JPEG) is a lossy compression for
images
Exploits the same kinds of humanperception characteristics that MP -3 does,only for light and color
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JPEG Compression
Humans are quite sensitive to smallchanges in brightness (luminance)
Brightness levels of a photo must bepreserved between uncompressed andcompressed versions
People are not sensitive to smalldifferences in color (chrominance)
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JPEG Compression
JPEG is capable of a 10:1 compressionwithout detectable loss of clarity simply bykeeping the regions small
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JPEG Compression
It is possible to experiment with levelsgreater than 10:1
The benefit is smaller files Eventually the picture begins to pixelate or
get jaggies
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MPEG Compression
MPEG is the same idea applied to motionpictures
It seems like an easy task Each image/frame is not seen for long Couldnt we use even greater levels of single -
image compression?
It takes many stills to make a movie
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MPEG Compression
In MPEG compression, JPEG-typecompression is applied to each frame
Interframe coherency is used Two consecutive video images are usually
very similar, MPEG compression only has torecord and transmit the differences between
frames Resulting in huge amounts of compression
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Optical Character Recognition
Very sophisticated technology thatenables a computer to read printedcharacters
OCRs business applications include: U.S. Postal Service processing up to 45,000
pieces of mail per hour (2% error rate)
In banking, the magnetic numbers at thebottom of checks have been read bycomputers since the 1950s
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Virtual Reality
Ultimate form of digital representation is tocreate an entire digital world
Rapidly displaying still images is astandard way to fool our eyes and braininto seeing motion
Virtual reality applies that idea to our othersenses and tries to eliminate the cues thatkeep us grounded in real reality
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Haptic Devices
Haptic devices are input/output technologyfor interacting with our sense of touch andfeel
For example: Haptic glove enables a computer to detect
where our fingers are and to apply force
against them, leaving us with the feeling ofholding an object
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Latency
The system must operate fast enough andprecisely enough to appear natural
Latency is the time it takes for informationto be delivered
Long latencies just make us wait, but longlatency can ruin the effect!
There is an absolute limit to how fastinformation can be transmitted the speedof light
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Bandwidth
Bandwidth is how much information istransmitted per unit time
Higher bandwidth usually means lowerlatency
VR is challenged by both latency andbandwidth limitations
Creating a synthetic world and delivering itto our senses is a difficult technicalproblem
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Bits Are It!
4 bytes can represent many kinds ofinformation
This a fundamental property ofinformation: B ias-Free Un iversal Mediu m Pr incip le :
Bi t s c an represent a ll d i sc re te inform at ion;
Bits have no inherent meaning
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Bits: The Universal Medium
All discrete information can berepresented by bits
Discrete things things that can beseparated from each other can berepresented by bits
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Bits: Bias-Free
Given a bit sequence0000 0000 1111 0001 0000 1000 0010 0000
there is no way to know what information itrepresents
Meaning of the bits comes entirely fromthe interpretation placed on them by usersor by the computer
Not Necessarily Binary
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Not Necessarily BinaryNumbers
Computers represent information as bits Bits can be interpreted as binary numbers Bits do not always represent binary
numbers ASCII characters RGB colors Or an unlimited list of other things
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Bits are bits
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Summary
We can use arithmetic on the intensities tocompute on the representation, for example,making gray lighter and colorizing a black-and-
white picture from the nineteenth century. When digitizing sound, sampling rate andmeasurement precision determine how accuratethe digital form is; uncompressed audio requires
more than 80 million bits per minute.
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Summary
Compression makes large files manageable:MP3 for audio, JPEG for still pictures, andMPEG for video. These compact representations
work because they remove unnecessaryinformation. Optical character recognition technology
improves our world.
Virtual reality illustrates the complexities ofconveying information to all of our sensessimultaneously.
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Summary
The Bias-Free Universal Medium Principleembodies the magic of computers throughuniversal bit representations and unbiased
encoding.