Chapter 1 Computer science

Lossless Compression

Loss less compression is when data is encoded to reduce file size + lossless compression is reversible + Reduce file size but not dramatic + Used when data lost is unsuitable like a full document or coding lines + Encodes pattern and repition 🔄

Binary Use - Digits

+ Data in computers are processed with logic gates which only have states 0/1 🤖

+ Computers is consist of millions of tiny switches with two logic gates -> It has two possible values only (0 [OFF] and 1 [ON]) 🔌

+ The binary system only has two digits 1/0 which means each digit can represents different state 0️⃣1️⃣

Denary

Base 10 number system 🔟

Denary Conversions

Subtracting by the binary board of number made by 2^n for denary

Hexadecimal

It is a base 16 system. 🔠

Why use hexadecimal

+ Often perferred to be use with large values

-> Fewer digits needed to represent given value

+ Easy to read by humans

+ Less prone to error when trying to input

Hexadecimal examples

E.g

+ MAC address

+ Colour codes

+ URL's 🔣

Binary addition

is the process of adding together two integer binary bits of 8 bits

-Rule of thumb

0+0 = 0

1+0 = 1

1+1 = 0 (remember 1)

1+1+1 = 1 (rem 1)

Overflow error (Addition)

could happen when the results of a binary addition exceeds the maximum bits operable in the computer ⚠️

Binary Shifts

is how a computer perform multiplication and division on numbers ✖️➗

Left shift

Moved left ( Left shift): Multiply that number by 2 and doubles the number value ⬅️

Right shift

Moved right (Right shift): Divides a number by 2 and halves the number by 2 ➡️

Binary Shift Increments

The number can keep moving and with an increment to multiply or divide by two 📈

Overflow error (Shifts)

An Overflow error can happen when a 1 is shifted out of the MSB on the left in a logical shift This means that important data will be lost and damage number value 🚫

Two's complement

is the "computer way" of using positive and negative values ➕➖

Two's complement Rule 1

In two's complement the LSB is designed as MSB 🥇

Two's complement Rule 2

MSB = -128 🔢

Two's complement Steps

Step 1: Workout the positive version of number Step 2: Invert all the bits Step 3: Add 1 in LSB 🪜

Text

Text is converted to binary to be processed by a computer 🔤

Character set

A standardized list use by computer using binary values to represent character and symbols 📋

ASCII

A standard character set accepted to represent english characters and math symbols and notations 🇺🇸

ASCII

+ A standard character set accepted to represent english characters and math symbols and notations

+ ASCII uses 7 bits or a maximum of 128 characters

Extended ASCII

an extended character set using 8 bits which is 256 unique codes and add essential characters with math operations and recent symbols

ASCII Limitations - Characters

Limited number of characters only represent English alphabet and some number and some special characters 📉

ASCII Limitations - Languages

Cannot other languages and does not include modern symbols or emojis too ❌

UNICODE

A characters set with a minimum of 16 bits which is over 65,536 characters it can represent 🌍

UNICODE Advantages

UNICODE can represent a wide range of characters from many major languages and a range of recent emojis and symbols ✨

UNICODE Disadvantages

But UNICODE required more bits per character than ASCII -> More storage space required 💾

Sound

Sound waves are captured as an analouge version -> convert to digital form with measurements of amplitude and in binary values. This is called Analogue to Digital conversion (ADC) 🎤

Sample Rate

Amount of samples taken per second of the analouge wave ⏱️

Sample resolution

Number of bits stored per sample 📏

Sample Rate Relationship

As Sample Rate increase, quality and accuracy of sound increases, but file size increase 📈

Sample Resolution Relationship

As Sample resolution increase, quality and accuracy of sound increases, but file size increase 📊

Sound file size calculation

Sample rate x duration x sample resolution 🧮

Images

Images are represent by computer as bitmaps that stores a specific binary values for each pixel with each picture have different size resolution and colour variety 📸

Pixel

Smallest element of a bitmap image ⬛

Resolution

Total amount of pixels in an image -> Height x Width 📏

Colour depth

Number of bits used to represent every colour 🎨

Amount of colours calculation

The amount of colours can be calculated as 2^n (n = colour depth) 🧮

Image File Size calculation

colour depth x resolution (width x height) 💾

File Size vs Resolution/Colour Depth

As the resolution and/or colour depth increases, the bigger the size of the file becomes on secondary storage 📈

Resolution vs Bits

The higher the resolution, the more pixels are in the image, the more bits are stored ⬆️

Colour Depth vs Bits

The higher the colour depth, the more bits per pixel are stored 🌈

Bit

1 or 0 1️⃣

Nibble

4 Bits 🧀

Byte B

8 Bits 🗃️

Kibibyte KiB

1,024 B (2^10) 📦

Mebibyte MiB

1,024 KiB (2^20) 📚

Gibibyte GiB

1.024 MiB (2^30) 🗄️

Tebibyte TiB

1,024 GiB (2^40) 🏢

Pebibyte PiB

1,024 TiB (2^50) 🏙️

Exbibyte EiB

1,024 PiB (2^60) 🌎

Conversion Tips

Tips - Multiply by 1024 to convert the units ✖️

Data Compresssion

Reduce size of files so that it takes up less space and secondary storage 🗜️

Data Compression Benefits

+ Less storage space needed

+ Less bandwidth required

+ Shorter transmission time ✅

Lossy Compression

Lossy compression is when data is Lost in order to reduce fie size 📉

Lossy Compression Characteristics

+ Lossy compression are irreversible

+ Reduce file size greatly -> more than lossless

+ Use when Reducing quality is acceptable

-> e.g: images, video, sound 🗑️

Lossy Sound (MP3)

-> Reduce file size by 90% -> Removes sounds outside human ear range -> Eliminates soft sound by perceptual music shaping 🎵

Lossy Video (MP4)

-> Reduce multimedia file size -> Allows movies to stream across web servers 🎬

Lossy Picture (JPEG)

-> Reduce file size of an image -> Human eyes cannot detect diffrences 🖼️

Lossless Compression

Loss less compression is when data is encoded to reduce file size 📦

Lossless Compression Characteristics

+ lossless compression is reversible

+ Reduce file size but not dramatic

+ Used when data lost is unsuitable like a full document or coding lines

+ Encodes pattern and repition 🔄

RLE (Run Length Encoding)

example of lossless compression

+ Reduce string of adjacent, identical items

RLE First Value

First value represent numbers of identical data 1️⃣

RLE Second Value

Second value represent ASCII of data item 2️⃣