Text, sound and images Revision Notes

Computers operate using binary data, which consists of sequences of 0s and 1s. To process text, which humans read as letters, digits, and symbols, computers must convert these characters into binary form. This conversion allows text to be stored, manipulated, and transmitted digitally.

Each character is assigned a unique binary code according to a character set. This standardisation ensures that text data is interpreted consistently across different devices and software.

For example, the letter 'A' might be represented as 01000001 in binary, which corresponds to a specific code in a character set. Without such a system, computers would not understand how to represent or display text.

• Text must be converted into binary for computer processing.
• Character sets assign unique binary codes to each character.
• Standardisation ensures consistent interpretation of text data.

ASCII (American Standard Code for Information Interchange) is one of the earliest character sets developed to represent text in computers. It uses 7 bits to encode each character, allowing for 2^7 = 128 unique codes.

These 128 codes include uppercase and lowercase English letters (A-Z, a-z), digits (0-9), common punctuation marks, and some control characters like newline and carriage return.

Because ASCII uses only 7 bits, it is efficient in terms of storage and processing. However, it is limited to basic English characters and cannot represent accented letters, symbols from other languages, or emojis.

• ASCII uses 7 bits per character, supporting 128 characters.
• Includes English letters, digits, punctuation, and control codes.
• Limited to basic Latin characters; cannot represent international symbols.
• Efficient for simple English text but insufficient for global use.

Unicode was developed to overcome ASCII's limitations by providing a universal character set that includes characters from most of the world's writing systems, as well as symbols, emojis, and special characters.

Unicode assigns a unique code point to each character, which can require more bits than ASCII. Common Unicode encodings include UTF-8, UTF-16, and UTF-32, which use variable or fixed numbers of bits per character.

For example, UTF-8 uses 8 bits for standard ASCII characters but can use up to 32 bits for less common characters. This flexibility allows Unicode to represent over a million different characters, supporting global communication and modern digital text needs.

• Unicode supports a vast range of characters from many languages and symbols.
• Uses variable-length encoding schemes like UTF-8, UTF-16, and UTF-32.
• Requires more bits per character than ASCII, increasing storage needs.
• Enables representation of emojis, accented letters, and non-Latin scripts.

ASCII and Unicode differ mainly in the number of characters they can represent and the number of bits used per character.

ASCII is limited to 128 characters using 7 bits, making it suitable for basic English text but inadequate for internationalisation.

Unicode supports over a million characters, requiring more bits per character, which increases file sizes and processing requirements but allows for comprehensive text representation worldwide.

• ASCII: 7 bits, 128 characters, limited to basic English.
• Unicode: variable bits (8 to 32), over a million characters, supports global scripts and symbols.
• Unicode increases storage and processing demands compared to ASCII.
• Choice depends on application needs: ASCII for simple text, Unicode for multilingual and symbol-rich text.

Using Unicode allows software and systems to handle text in any language, which is essential in today's globalised world.

However, because Unicode characters can require more bits, files containing Unicode text are often larger than ASCII equivalents, affecting storage and transmission.

Programmers and system designers must consider encoding when processing text data to avoid errors such as misinterpreted characters or data corruption. For example, reading a Unicode file as ASCII can result in incorrect characters being displayed.

• Unicode enables global text representation, including emojis and symbols.
• Larger bit requirements increase file size and processing time.
• Correct encoding handling is essential to prevent text errors.
• Systems must support Unicode to ensure compatibility with international text.

Sound in the real world is an analogue wave, continuously varying in air pressure. Computers, however, can only process discrete binary data (0s and 1s). To handle sound, computers convert these continuous waves into digital form through a process called sampling.

Sampling involves measuring the amplitude (height) of the sound wave at regular intervals. Each measurement is then converted into a binary number, allowing the computer to store and manipulate the sound digitally.

This conversion enables sound to be stored, edited, and transmitted by computers, but the quality of the digital sound depends heavily on how the sampling is done. Two key factors are sample rate and sample resolution.

• Sound waves are continuous analogue signals.
• Computers require discrete binary data to process sound.
• Sampling converts analogue sound into digital binary data.
• Quality depends on sampling parameters.

Sample rate is the number of times per second the sound wave is measured. It is measured in hertz (Hz), where 1 Hz equals one sample per second.

For example, a sample rate of 44,100 Hz (44.1 kHz) means the sound wave is sampled 44,100 times every second. This is the standard sample rate for audio CDs.

A higher sample rate captures more detail from the original sound wave, allowing more accurate reproduction of high-frequency sounds. Conversely, a low sample rate may miss important details, causing the sound to lose clarity or introduce distortion known as aliasing.

• Sample rate is measured in samples per second (Hz).
• Higher sample rates capture more sound detail.
• Standard CD quality uses 44,100 Hz.
• Low sample rates reduce sound quality and can cause distortion.

Sample resolution, also called bit depth, is the number of bits used to represent each individual sample's amplitude.

For example, a sample resolution of 8 bits means each sample can have 2^8 = 256 possible amplitude values. A 16-bit resolution allows 2^16 = 65,536 values, providing much finer detail in representing the sound's loudness at each sample point.

Higher bit depths reduce quantisation error, which is the difference between the actual analogue amplitude and its digital representation. This results in clearer, more accurate sound with less noise and distortion.

• Sample resolution is the number of bits per sample.
• Higher bit depth means more precise amplitude representation.
• 8-bit allows 256 levels; 16-bit allows 65,536 levels.
• Higher resolution reduces quantisation noise.

Increasing either sample rate or sample resolution improves the accuracy and quality of the digital sound recording. However, this also increases the amount of data generated, leading to larger file sizes.

The size of a digital sound file can be calculated using the formula:

File size (bits) = Sample rate (samples/second) × Sample resolution (bits/sample) × Duration (seconds) × Number of channels (e.g., 2 for stereo).

• Higher sample rate and resolution improve sound quality.
• Larger values increase file size proportionally.
• File size depends on sample rate, resolution, duration, and channels.
• Example: A 3-minute stereo recording at 44,100 Hz and 16-bit resolution:
• File size = 44,100 × 16 × 180 × 2 = 254,016,000 bits ≈ 30.25 MB (using 8 bits per byte and 1024 bytes per KiB).

Consider a mono (single channel) recording lasting 10 seconds, sampled at 8,000 Hz with 8-bit resolution.

Calculate the file size in bits: File size = 8,000 samples/second × 8 bits/sample × 10 seconds × 1 channel = 640,000 bits.

Convert bits to bytes: 640,000 bits ÷ 8 = 80,000 bytes ≈ 78.13 KiB (since 1 KiB = 1024 bytes).

• Mono recording: 10 seconds, 8,000 Hz, 8-bit.
• File size = 8,000 × 8 × 10 × 1 = 640,000 bits.
• In bytes: 640,000 ÷ 8 = 80,000 bytes.
• In KiB: 80,000 ÷ 1024 ≈ 78.13 KiB.

Digital sound quality depends on sample rate and sample resolution. Higher values improve fidelity but increase file size and storage requirements.

Choosing appropriate values depends on the application: telephone audio uses low sample rates and resolutions to save bandwidth, while professional music recording uses high values for quality.

Understanding these concepts helps in making informed decisions about sound recording and storage in computing.

• Sample rate and resolution balance quality and file size.
• Higher values mean better sound but larger files.
• Application determines suitable sampling parameters.
• Knowledge aids efficient sound data handling.

Digital images are composed of tiny units called pixels (picture elements). Each pixel represents a single point in the image and holds information about its colour.

To be processed and stored by a computer, each pixel's colour information is converted into binary code — a sequence of bits (0s and 1s). This binary representation allows the computer to manipulate, display, and save the image.

For example, a pixel might be represented by 8 bits, where each bit pattern corresponds to a specific colour or shade. The more bits used per pixel, the more colours can be represented, resulting in richer images.

• Pixels are the smallest units of a digital image.
• Each pixel's colour is converted into binary for computer processing.
• More bits per pixel allow more colours to be represented.

Resolution refers to the total number of pixels in an image, usually expressed as width × height (e.g., 1920 × 1080 pixels).

Higher resolution means more pixels, which generally leads to sharper and more detailed images because there are more points of colour information.

However, increasing resolution also increases the amount of data needed to store the image, thus increasing the file size. This means higher resolution images require more storage space and more processing power to handle.

• Resolution = width × height (number of pixels).
• Higher resolution improves image detail and sharpness.
• Higher resolution increases file size.

Colour depth is the number of bits used to represent the colour of each pixel. It determines how many different colours can be displayed in an image.

For example, a colour depth of 1 bit per pixel can represent 2 colours (usually black and white). An 8-bit colour depth can represent 2^8 = 256 colours, while a 24-bit colour depth can represent 2^24 ≈ 16.7 million colours, allowing for very detailed and realistic images.

Increasing colour depth improves image quality by allowing more colour variations but also increases the file size proportionally.

• Colour depth = bits used per pixel to represent colour.
• Higher colour depth means more colours and better image quality.
• File size increases as colour depth increases.

The size of an uncompressed image file in bits can be calculated using the formula:

Image size (bits) = width (pixels) × height (pixels) × colour depth (bits per pixel)

To convert bits to bytes, divide by 8 (since 1 byte = 8 bits). For example, to find the size in kilobytes (KB), divide bytes by 1024 (1 KB = 1024 bytes). This helps estimate storage requirements and bandwidth for image files.

• Image size (bits) = width × height × colour depth.
• Divide bits by 8 to get bytes.
• Divide bytes by 1024 to get kilobytes (KB).

Suppose you have an image with a resolution of 800 pixels wide and 600 pixels high, and a colour depth of 24 bits per pixel.

Step 1: Calculate total pixels: 800 × 600 = 480,000 pixels.

Step 2: Calculate total bits: 480,000 pixels × 24 bits/pixel = 11,520,000 bits total file size (uncompressed). Step 3: Convert bits to bytes: 11,520,000 bits ÷ 8 = 1,440,000 bytes. Step 4: Convert bytes to kilobytes: 1,440,000 bytes ÷ 1024 ≈ 1406.25 KB (about 1.4 MB). This means the image requires approximately 1.4 megabytes of storage without compression.

• Calculate pixels: width × height.
• Multiply pixels by colour depth to get bits.
• Convert bits to bytes and then to KB or MB.
• Check units carefully to avoid errors.

Increasing resolution and colour depth improves image quality by adding detail and colour richness, but this also increases the file size, requiring more storage and bandwidth.

When designing or choosing images for specific uses, such as web pages or printing, it is important to balance quality and file size.

For example, a web image might use lower resolution and colour depth to load quickly, while a professional photograph might use high resolution and colour depth for clarity and colour accuracy.

• Higher resolution and colour depth improve quality but increase file size.
• Balance image quality with storage and bandwidth constraints.
• Choose settings based on the intended use of the image.