Data Storage & Compression (OCR GCSE Computer Science)

True.

When converting between units bigger than a byte, multiples of 1000 are used.

True.

There are 8 bits in a byte.

Data has to be converted to binary to be processed by a computer because computers are built using switches that can either be on or off, which fits the binary number system (1/0).

Binary data is data represented using the binary number system, consisting of only two digits: 1 and 0.

Computers are built using circuits and transistors. The circuits only need to check for two states:

• On / off

• True / False

• North / south

• 1 / 0

The binary number system is a base-2 number system that represents numbers using only two digits: 0 and 1.

An example of binary representation in secondary storage devices is magnetic hard drives, where North and South polarity represent 1 and 0.

A transistor is a semiconductor device used to amplify or switch electronic signals and electrical power.

Processing data in binary format allows computers to perform complex calculations, store vast amounts of data efficiently, and operate at incredible speeds.

Secondary storage refers to non-volatile storage devices that retain data even when the computer is turned off, such as hard drives and optical disks.

Data capacity is the maximum amount of information that a storage device can hold.

False.

Data capacity refers to the maximum storage capacity of a device, while file size refers to the amount of storage used by a specific file.

To calculate capacity requirements, you need to know:

• the capacity of the storage device

• the size of the file(s) being stored.

Size of text file = (Bytes per character) x (Number of characters)

Size of image file = (Resolution) x (Colour Depth)

Resolution is the total amount of pixels that make up a bitmap image, calculated by multiplying the height and width of the image (in pixels).

Colour depth is the number of bits stored per pixel in a bitmap image.

Size of sound file = (Sample Rate) x (Duration in seconds) x (Bit Depth)

Sample rate is the amount of samples taken per second of an analogue sound wave.

Bit depth is the number of bits stored per sample of sound.

Denary is a number system that is made up of 10 digits (0-9), also known as the Base-10 number system.

False.

Humans use the denary system for counting, measuring, and performing math calculations.

Binary is a number system that is made up of two digits (1 and 0), also known as the Base-2 number system.

Computers use binary numbers to represent data because they are built using transistors that can only be in a state of on or off, which fits the binary number system of 1s and 0s.

False.

At GCSE level, you will only be asked to convert from/to binary up to and including 8 binary digits (8 bits).

At GCSE level, you will work with a denary range of 0-255 (00000000-11111111 in binary).

128+0+32+0+8+4+0+0=172

0+64+32+16+0+0+0+1=113

128+0+0+16+0+0+0+0=144

Binary addition is the process of adding together two binary integers (up to and including 8 bits).

True.

Binary addition starts from the rightmost digit and moves left.

Carrying over occurs when the sum of a column is greater than 1, passing the excess to the next left column.

An overflow error is when the result of a binary addition exceeds the available bits.

1 + 1 = 10 (2 in denary)

True.

The sum of 1001 and 0100 in binary is 1101.

The sum of 00011001 and 10000100 in binary is 10011101.

A binary integer is a number represented in the base 2 number system.

Hexadecimal is a number system with 16 digits.

True.

Hexadecimal uses 16 digits.

One hexadecimal digit represents four bits of binary data.

A nibble is four bits of binary data or half a byte.

Convert denary to binary, split into nibbles, then convert each nibble to hexadecimal.

Divide denary by 16 and record the whole times (hex digit 1) and remainder (hex digit 2).

Example: Denary 160 to Hexadecimal = 160/16 = 10 (A) r 0 = A0

Convert each hexadecimal digit to 4-bit binary, join nibbles, then convert binary to denary.

Multiply the first digit by 16, then add the second digit.

Example: Hexadecimal B4 to denary = B (11) x 16 = 176 + 4 = 180

Convert 45 to binary

Split in to 2 nibbles (4-bits)

0010 = 2
1101 = 13 (D)
Answer = 2D

Convert each digit to 4-bit binary (nibble)

Combine to create an 8-bit value and convert to denary
11100111 = 231

A nibble is a group of four bits.

Binary is a base-2 number system that uses only 0 and 1 to represent all values.

True.

Each digit in a hexadecimal number represents a binary nibble.

Denary is the base-10 number system, which uses digits from 0 to 9.

Hexadecimal is a base-16 number system that uses 16 digits: 0-9 and A-F.

Method:

• Split X (binary number) into 2 x nibbles (4-bits)

• Convert each nibble to its denary value

• Represent each denary value as a hexadecimal digit.

Example: 11011001 = 1101 (13=D), 1001 (9) = D9

True.

To convert hexadecimal to binary, you split the digits apart and convert each to a binary nibble.

A bit is a basic unit of information in computing, representing either a 0 or 1.

A byte is a unit of digital information that consists of 8 bits.

Split in to 2 nibbles (4-bits)

1110 = 14 (E)
1000 = 8
Answer = E8

Convert each digit to a 4-bit binary value

Combine to create an 8-bit value
Answer = 01001011

A binary shift is how a computer system performs basic multiplication and division by moving binary digits left or right a set number of times.

A left shift multiplies a binary number by 2 (for every 1 place shift).

True.

A right shift divides a binary number by 2 (for every 1 place shift).

An overflow error occurs when a result is too large to be represented in the available storage space (needs more bits).

An underflow error occurs when a result is too small to be represented in the available storage space.

X << 2 (X shifted left by 2 places) = X * 4

True.

A left shift of 3 places multiplies the binary number by 8.

In binary shifts, precision refers to the number of digits used to represent a value.

A bit is a basic unit of information in computing, representing either a 0 or 1.

Binary multiplication refers to the process of multiplying binary numbers using shifts and additions.

A character set is a defined list of characters that can be understood by a computer, where each character is given a unique binary code.

ASCII (American Standard Code for Information Interchange) is a character set that uses 7 bits, allowing it to represent a maximum of 128 characters.

False.

ASCII can only represent basic characters needed for English, limiting its use for other languages.

Extended ASCII is a character set that uses 8 bits, allowing it to represent a maximum of 256 characters.

UNICODE is a character set that uses a minimum of 16 bits, allowing it to represent a minimum of 65,536 characters from all major languages around the world.

A bit is a single binary digit, representing either a 0 or 1.

True.

UNICODE uses more storage space than ASCII because it uses more bits to represent characters.

Character encoding refers to the process of assigning a unique binary code to each character in a character set.

True.

A drawback of ASCII is that it cannot represent special characters such as emoji's (not enough bits)

Maximum number of characters = (to the power of the number of bits)

A bitmap is an image made up of squares called pixels, where each pixel is stored as a binary code.

A pixel is the smallest element of a bitmap image.

True.

A vector image is created from mathematical equations and points.

Resolution is the total amount of pixels that make up a bitmap image, calculated by multiplying the height and width of the image (in pixels).

Colour depth is the number of bits stored per pixel in a bitmap image.

Maximum number of colours = (2 to the power colour depth)

Example: Colour depth = 8, 2⁸ = 256 colours

True.

Higher resolution and colour depth result in larger file sizes.

Metadata is data about data, or additional information stored with an image but not required to display the image.

True.

A vector image is scalable, it is able to be resized or changed in size without loss of quality.

False.

A vector image is created from mathematical equations and points, not pixels.

True Colour refers to a colour depth of 24 bits, allowing for 16,777,216 colours.

Pixel depth is another term for colour depth, referring to the number of bits used to represent the colour of each pixel.

Sampling is the process of measuring and capturing values of an analogue signal at regular intervals.

An analogue signal is a continuous signal that can take on any value within a range, such as a sound wave.

True.

Sound must be converted from analogue to digital form for a computer to process it.

Analogue to Digital conversion (A2D) is the process of converting an analogue signal, such as sound, into a digital form that a computer can understand.

Sample rate is the amount of samples taken per second of an analogue signal, measured in Hertz (Hz).

Bit depth is the number of bits stored per sample of sound.

True.

Higher sample rate and bit depth result in better sound quality but larger file sizes.

Hertz (Hz) is the unit of measurement for frequency, equal to one cycle per second.

Amplitude refers to the height of a sound wave.

True.

The sample rate of a typical audio CD is 44.1 kHz (44,100 samples per second).

Digital refers to data represented as a series of discrete values, typically 0s and 1s in binary.

Duration is the length of time that something lasts, in the context of sound, it refers to the length of the recorded audio.

Compression is reducing the size of a file so that it takes up less space on secondary storage.

Lossy compression is when data is lost in order to reduce the size of a file on secondary storage.

True.

Lossy compression is irreversible.

Irreversible means something that cannot be undone or reversed to its original state.

Lossless compression is when data is encoded in order to reduce the size of a file on secondary storage.

True.

Lossless compression can be used on all data types without losing quality.

Encoding is the process of converting data into a specific format or code.

True.

Lossy compression can greatly reduce file size but at the expense of losing quality.