Data representation

Question 1

Binary

Answer 1

Base 2 number system used by computers.

Question 2

Denary/Decimal

Answer 2

Base 10 number system used by humans.

Question 3

Bit

Answer 3

A single binary digit. (1 or 0) (b)

Question 4

Nibble

Answer 4

4 binary digits showing the values between 0 – 15 (0000 – 1111)

Question 5

Byte

Answer 5

8 binary digits. Can represent numbers from 0 to 255 (B)

Question 6

Kilobyte

Answer 6

1024 bytes

Question 7

Megabyte

Answer 7

1024 Kilobytes

Question 8

Hexadecimal

Answer 8

Base 16 Number system, used for easier reading of binary (0 - 9 and A – F).

Question 9

Character Set

Answer 9

All the characters and symbols that can be recognised by a computer system.

Question 10

ASCII

Answer 10

Character set for the English language 7bit character set with 128 values represented.

Question 11

Extended ASCII

Answer 11

8Bit character set with 256 values represented

Question 12

Unicode

Answer 12

Character set for all languages 16 bit

Question 13

Overflow Error

Answer 13

When the result of adding binary goes beyond 8 bits / 1 byte

Question 14

Compression

Answer 14

The process of making the size of a file smaller.

Question 15

Lossless Compression

Answer 15

Makes the file smaller by temporarily removing data to store the file and then restores it to its original state when opened. EG: Huffman Encoding or RLE. Mainly used in compression of text.

Question 16

Lossy Compression

Answer 16

Works by permanently removing data from the file. This limits the number of bits the file needs and reduces its size. Mainly used in compression of sound or images.

Question 17

Run Length Encoding

Answer 17

A form of Lossless compression. RLE is a simple method of compressing data by specifying the number of times a character or pixel colour repeats followed by the value of the character or pixel.

Question 18

Huffman Encoding

Answer 18

A form of Lossless compression. Huffman coding is a compression technique used to reduce the number of bits needed to send or store text.

Question 19

Pixel

Answer 19

Short for Picture Element. A pixel is a single dot of an image. It is usually rectangular or square and is the smallest element that can be displayed

Question 20

Colour Depth

Answer 20

The number of bits used to represent each pixel in a bitmap image.

Question 21

Metadata

Answer 21

The information stored in an image file which helps the computer to recreate the image on screen from the binary data. It includes, file format, height, width, colour depth and resolution.

Question 22

Bitmap

Answer 22

An image can be represented as a bitmap. It is divided into pixels, each colour is represented by a unique bit pattern, the colour of each pixel and metadata about the image is then stored.

Question 23

Sample

Answer 23

A measure of amplitude at a point in time

Question 24

Sample Rate

Answer 24

The number of samples taken in a second and is usually measured in Hertz (1 Hertz = 1 sample per second).

Question 25

Sample Resolution

Answer 25

The number of bits used to represent each sample.

Question 26

High sample resolution

Answer 26

Better quality as more measurements are taken per second. Large file size which takes up more storage space. Uses more processing power when recording with a high sample resolution.

Question 27

Hertz

Answer 27

One sound sample per second

Question 28

Conversion of sound wave be stored

Answer 28

The height and amplitude of a sound wave is measured at set intervals and stored as a binary value

Question 29

Calculate bitmap image file sizes

Answer 29

Size in bits = Width x Height x Colour Depth

Question 30

Size in bytes = (Width x Height x Colour Depth) /8

Question 31

Calculate sound file sizes

Answer 31

Size in bits = sampling rate x sample resolution x number of seconds

Question 32

Bit Rate

Answer 32

Sample rate x Sample Resolution x Number of Channels

Question 33

Number of bits saved when encoding in Huffman rather than ASCII

Answer 33

ASCII size = 7 x Number of characters (including spaces and punctuation) Huffman size = total number of bits used for each for reach character x the number of characters each bit represents. Bits saved = ASCII size – Huffman size.