Mastering Data Representation: Text, Sound, and Images for IGCSE Computer Science 0478

 

Text, Sound, and Images

Table of Contents

1. Text, Sound, and Images
   • 1.1 Text Representation
     • 1.1.1 Introduction to Binary Encoding
     • 1.1.2 ASCII Character Set
     • 1.1.3 Unicode Character Set
   • 1.2 Sound Representation
     • 1.2.1 Sampling and Binary Conversion
     • 1.2.2 Sample Rate and Sample Resolution
   • 1.3 Image Representation
     • 1.3.1 Pixels and Binary Encoding
     • 1.3.2 Resolution and Colour Depth

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1. Text, Sound, and Images

In this section, we will explore how computers represent different types of data, including text, sound, and images. Computers process data in binary format (0s and 1s), so each type of data needs to be converted into binary to be processed and stored by computers effectively.

1.1 Text Representation

Text is an essential data type in computing, and computers represent it in binary using character encoding standards. The most commonly used standards are ASCII and Unicode.

1.1.1 Introduction to Binary Encoding

• Computers cannot directly understand text or characters. They represent all information in binary form (1s and 0s).
• Each character is assigned a specific binary code. For instance, the character "A" is represented in binary as 01000001.

1.1.2 ASCII Character Set

• ASCII stands for American Standard Code for Information Interchange.
• ASCII is a 7-bit character encoding standard, meaning it uses 7 bits for each character, which allows for 128 unique characters.
• These 128 characters cover basic English letters, digits, and some symbols. For example:
  • "A" in ASCII is 01000001.
  • "B" in ASCII is 01000010.
• ASCII is limited to English letters and basic symbols, which restricts its usability for representing different languages or symbols beyond English.

1.1.3 Unicode Character Set

• Unicode was developed to represent a broader range of characters, including those from non-Latin alphabets (e.g., Chinese, Arabic, and Cyrillic scripts) as well as symbols and emojis.
• Unicode is a flexible standard and can use different encoding formats, such as UTF-8, UTF-16, and UTF-32.
  • UTF-8 is commonly used on the internet, where characters are represented with 1 to 4 bytes.
• Unicode requires more bits per character than ASCII, which results in larger file sizes but enables support for a vast array of characters and symbols.

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1.2 Sound Representation

Sound is represented digitally by capturing the characteristics of sound waves and converting them into binary data.

1.2.1 Sampling and Binary Conversion

• A sound wave is a continuous signal, and to digitize it, the sound wave is sampled at specific intervals.
• Each sample represents the amplitude (or intensity) of the sound wave at a particular moment in time.
• These samples are then converted into binary numbers, which allows the sound to be processed and stored by the computer.

1.2.2 Sample Rate and Sample Resolution

• Sample Rate: The sample rate, measured in Hertz (Hz), is the number of samples taken per second.
  • A higher sample rate increases the accuracy of the digital sound representation but also increases the file size.
  • Example: A sample rate of 44,100 Hz means that 44,100 samples are taken per second.
• Sample Resolution: This is the number of bits used for each sample, affecting the precision of each sample’s binary value.
  • A higher sample resolution (e.g., 16-bit or 24-bit) results in better sound quality because each sample can capture more detailed information about the sound wave.
• Impact on Quality and File Size: As sample rate and resolution increase, the quality and accuracy of the sound improve, but this also results in larger file sizes.

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1.3 Image Representation

Images are represented by converting visual information into binary form, using a structure based on pixels and color depth.

1.3.1 Pixels and Binary Encoding

• Pixel: A pixel is the smallest unit of an image and represents a single color in the overall picture.
• Each pixel is assigned a binary code that indicates its color. When combined, these pixels create the entire image.

1.3.2 Resolution and Colour Depth

• Resolution: Resolution is defined by the number of pixels in an image, often measured as width x height (e.g., 1920x1080).
  • Higher resolution means more pixels, resulting in better image clarity and detail but also larger file sizes.
• Colour Depth: Colour depth refers to the number of bits used to represent the color of each pixel.
  • Common color depths include 8-bit (256 colors), 16-bit (65,536 colors), and 24-bit (16 million colors).
  • Higher color depth allows for a broader range of colors, making the image appear more realistic and vibrant.
• Impact on Quality and File Size: As resolution and color depth increase, the visual quality of the image improves, but this also increases the image’s file size.