Edexcel GCSE Computer Science: Data Representation

Data Representation in GCSE Computer Science

This tutorial explores how data is represented in binary, focusing on text, images, and sound.

Text Representation: ASCII Encoding

• Binary: Computers store data in binary, using only 0s and 1s.
• Characters: Text is made up of characters, like letters, numbers, and symbols.
• ASCII: The American Standard Code for Information Interchange (ASCII) assigns a unique binary code to each character.
  • For example, the letter 'A' is represented by the binary code 01000001.
• Limitations: ASCII only covers a limited set of characters, primarily English alphabet and common symbols.

Image Representation

• Pixels: Images are made up of tiny squares called pixels.
• Resolution: Resolution refers to the number of pixels an image contains. Higher resolution means more pixels and a sharper image.
• Color Depth: The number of colors a pixel can display is determined by its color depth.
  • For example, 8-bit color depth allows for 256 different colors (28).
• File Size: Image file size depends on resolution and color depth. Higher resolution and color depth result in larger file sizes.

Sound Representation

• Sampling: Sound is recorded by taking samples at regular intervals.
• Sample Rate: The number of samples taken per second determines the sample rate. Higher sample rates capture more sound information and result in higher fidelity.
• Bit Depth: Similar to color depth in images, bit depth for sound determines the range of volume levels a sample can represent.
• File Size: Sound file size depends on sample rate, bit depth, and duration of the recording.

Data Compression Techniques

• Compression: Reducing the size of a file without losing data (lossless) or some data (lossy).
• Lossless Compression: Methods like Run-Length Encoding (RLE) and Huffman coding identify patterns in data and replace them with shorter codes, preserving all original data.
• Lossy Compression: Methods like JPEG and MP3 sacrifice some data for smaller file sizes. They remove less important data that is less noticeable to humans.

File Size Calculation

• Text: File size is approximately the number of characters multiplied by the number of bits per character (typically 8 for ASCII).
• Image: File size depends on the resolution and color depth. It can be calculated by multiplying the number of pixels by the number of bits per pixel.
• Sound: File size depends on sample rate, bit depth, and duration of the recording. It can be calculated by multiplying the sample rate by the bit depth by the duration in seconds.

Examples

• ASCII Example: The text "Hello" is represented by the following ASCII codes: 01001000 01100101 01101100 01101100 01101111
• Image Example: A 1000x1000 pixel image with 24-bit color depth (16.7 million colors) requires 3 million bytes (1000 * 1000 * 3 bytes per pixel) of storage.
• Sound Example: A 1-minute sound recording at 44.1kHz sample rate and 16-bit depth requires approximately 6.4MB of storage.

Conclusion

Understanding data representation is crucial in computer science. It provides a foundation for comprehending how computers store and process various forms of data, including text, images, and sound. By learning about ASCII encoding, image resolution, color depth, sample rates, and compression techniques, students gain insights into the underlying principles of digital information.