1.3 Image/Raster/Vector/Audio/Video

✅ INT, Decimal, CHAR

1. Positive INT
2. Negetive INT: add sign flag
3. Decimal
4. Characters: ASCII, UTF-8 8bits = 1byte 3bytes: Korean

✅ Image

☑️ Two types of pictures

1️⃣ Raster

• divided into pixels, get pixelized when increase size
• JPG, gig, png
• HR: Horizontal Resolution: how many pixels in horizontal
• VR: Vertical Resolution: how many pixels in vertical
• Resolution = HR * VR

✔️ Color Depth in Raster

• Color Depth: how many bits you need for your image
• 1 or 8 or 24
• black and white ➡️ need 2 combos, need 1 bit(2^1) ➡️ color depth: 1
• gray-scaled ➡️ need 256 combos, need 8 bits(2^8) ➡️ color depth: 8

• full-colored ➡️ every color is R(0~255) + G(0~255) + B(0~255) ➡️ each R, G, B need 8 bits ➡️ need 24 bits ➡️ color depth: 24

  • pure red would be 255.0.0
  • pure green would be 0.255.0
  • black would be 0.0.0
  • white would be 255.255.255
  • as soon as there is one colored pixel, even if all pixels are black and white, color depth will be 24
  • if full color, each pixel will have 24bits, graphic card will write the bits
• color codes are stored in graphic cards, in the driver
• Red = 111111110000000000000000
• I can use up to 2^24 colors, more or less 16000000(16 million)
  • (2^4)*(2^10)*(2^10) = more or less 16 _ 1000 _ 1000

• If a graphic card had 64bytes, it can have 2^64 colors

  • (2^4)*(2^10)*(2^10)*(2^10)*(2^10)*(2^10)*(2^10)

✔️ Picture size in Raster

• Picture size = HR * VR * D(color depth)

• Q: How many bits would a black and white picture have of 100*50?
• Picture size = HR * VR * D(color depth)

• 100 _ 50 _ 1 = 5000

• Q: How many bits would a gray picture have of 100*50?
• Picture size = HR * VR * D(color depth)

• 100 _ 50 _ 8 = 40000

• Q. If all the sound that my computer can make are 8, how many bits do my computer have?
• 3

• because 2^3 = 8

• Q: How many bits would a full color picture have of 100*50?
• 100 _ 50 _ 24 = 120000

2️⃣ Vector

• more professional, more efficient when picture is not so detailed

• svg

• do not use pixel, use area by area/ zone by zone
• call the color just once, then call the length/size/measurements of the area that has that color
• red(255.0.0)* 10 meters + white(255.255.255) * 2 meters
• the picture tends to be more compressed
• 👍🏻 more efficient
• 👍🏻 when the area is uniform, no big transition of color, use vector
• ⭐️ big areas of same color(like football field, all green)
• ↔️ image with many color changes, use Raster(like a christimas tree with lights)
• if I make the picture bigger, zoom in, then just have to change the length/size/measurements

• do not get pixelized

• 👎🏻 however, as measuring device is more expensive,
• vector is only used for professional purposes

✅ Main Computing Principle

• 0: 0.5v

• 1: 5v

• If you have n bits to store 0s and 1s, you can have total of 2^n combinations
• 1 bit: 2 combinations 0, 1
• 2 bits: 4 combinations 00, 01, 10, 11

✅ Audio

• Audio is vibration, need air to transmit
• microphone has a small surface to capture vibration
• microphone transforms the vibration to binary code
• extension for audio is .wav
• when compressed/summarized .mp3
• if la, la, la, la, .mp3 summarizes sound and save la * 4
• ⭐️ .mp3 does not lose sound, but just summarized
• .mp3 is very compressed when there are uniform, repetitive sounds in the track
• if many different sounds, .mp3 will not be so efficient

☑️ Sampling Frequency(Hertz)

• how many times per second the computer pays attention to the microphone/vibration
• sampling frequency ⬆️ better sound quality, capture more details of the sound wave ⬆️
• sampling frequency ⬇️ cannot capture all the details of the sound ⬇️
• if frequency is 1Hz, then listening 1 time/sec
• if frequency is 1KHz, then listening 1000times/sec
• if frequency is 1MHz, then listening 1000000 million times/sec
• if frequency is 1GHz, then listening 1000000000 thousand million times/sec
• ⭐️ If you capture a sound with a high sampling frequency, will get more details of the sound

☑️ Amplitudes(bits)

• number of all the different sounds you want to capture
• in an orchestra, you want to capture more instruments, need higher amplitude

• higher amplitude ⬇️ more variety of sounds ⬇️

• Q: If orchestra used 16 different sounds, how many bits would we need?
• need 4 bits

• Amplitude = 4

• Q: If orchestra used 80 different sounds, how many bits would we need?
• need 7 bits (2^7 = 128)
• Amplitude = 7

☑️ Time(seconds)

• how long the music lasts, measured in seconds
• more time ⬆️ can save longer song ⬆️

☑️ Stereo

• full stereo: use two sequences of bits(2bits) per ear, duplicate the sound
• left and right ear listen to the whole song

• need to multiply formula by 2

• mono stereo: one sequence of bit, divide sound in two
• left ear: background music, right ear: singer voice

✔️ Audio Size

• size: Hertz _ A _ t

• Q: How many bits do you need for an orchestra for a 4 minute song, with 300 amplitude, sampled at 2GHz?
• size: Hertz _ A _ t
• 2*10^9 * 9 * (4*60)
• = 2000000000 * 9 * 240 bits

✅ Video

• sequence of pictures + sound
• .avi, .mov
• compressed into .mp4
• however, .mp4 suffered from forking
• forking: people started using another form of video .wmv, not compatible with .mp4

☑️ FPS, Frames per second

• how many images video shows per second
• fps ⬆️ better quality of video ⬆️

☑️ Prediction in video

• in video use prediction
• as some pictures are important,
• but other pictures are evident, so not as important
• use picture I, P, B

✔️ Types of picture in video

• Picture type I: Important
• independent pictures, important pictures

• mandatory to record them independently

• Picture type P: Predicted
• predicted pictures

• can predict them, as it is very similar to the previous picture

• Picture type B: Bidirectional
• Bidirectional
• extra movement to make video transition more natural
• pictures used to improve transitions
• make video look softer

✔️ Video size

• picture size _ fps _ t + Audio *2(stereo)
• HR * VR * D * fps * t + Hertz*A*t*2
• ⭐️ audio is added
• Q: How many bits a 5 minute video have that has resolution of 1920*1090 full color at 1000fps with stereo sound of 100 amplitudes sampled at a frequency of 25MHz?
• (1920*1090 * 24)1000(560) + (2510^6)7(5*60)*2 bits
• ⭐️ only depth and amplitude has to be converted into bits

✅ File

• all the bits are stored in a unit called file

• file: set of bits with information

• metadata: extra information about the file
• ⭐️ metadata is stored in the Operating system

• Timestamp: created_date, last_modified_date

  • owner of the file
  • size of the file
  • name of the file
  • extension of the file
  • permissions over the file, read/write/execute permissions, what you can do with the file
  • access control, who can access the file
• Linux keeps metadata very far from data itself, 👍🏻 safe from haking
• However, Windows keep metadata close to the data itself, not very safe

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