Data Compression Techniques to Know for AP Computer Science Principles

Data compression techniques are essential for efficiently storing and transmitting information. Understanding methods like run-length encoding, Huffman coding, and the differences between lossless and lossy compression helps optimize data management in computer science applications.

1. Run-length encoding (RLE)

   • Simplifies data by replacing consecutive repeated values with a single value and a count.
   • Effective for data with many repeated elements, such as simple graphics or monochrome images.
   • Not efficient for data with high variability, as it can increase file size.

2. Huffman coding

   • A variable-length coding scheme that assigns shorter codes to more frequent symbols and longer codes to less frequent ones.
   • Builds a binary tree based on the frequency of each symbol in the data.
   • Widely used in file formats like ZIP and JPEG for efficient data compression.

3. Lossless vs. lossy compression

   • Lossless compression retains all original data, allowing for perfect reconstruction (e.g., PNG, FLAC).
   • Lossy compression reduces file size by permanently removing some data, often resulting in quality loss (e.g., JPEG, MP3).
   • The choice between the two depends on the application and acceptable quality trade-offs.

4. Dictionary-based compression (e.g., LZW algorithm)

   • Utilizes a dictionary of previously seen sequences to replace repeated patterns with shorter codes.
   • Commonly used in formats like GIF and TIFF.
   • Efficient for text and data with repetitive patterns, but can be less effective on highly random data.

5. Delta encoding

   • Stores differences between sequential data points rather than the complete data.
   • Useful for time-series data or video frames where changes are minimal between frames.
   • Reduces storage requirements by only saving the changes, rather than the entire dataset.

6. Image compression techniques (e.g., JPEG)

   • JPEG uses lossy compression to reduce file size by discarding less important visual information.
   • Employs techniques like chroma subsampling and discrete cosine transform (DCT) to achieve compression.
   • Balances image quality and file size, making it ideal for photographs and web images.

7. Audio compression techniques (e.g., MP3)

   • MP3 uses lossy compression to reduce file size by removing inaudible frequencies and redundant data.
   • Employs perceptual coding to prioritize sounds that are more important to human hearing.
   • Widely used for music and audio streaming due to its balance of quality and size.

8. Video compression techniques (e.g., MPEG)

   • MPEG uses lossy compression to reduce video file sizes by eliminating redundant frames and data.
   • Combines techniques like inter-frame and intra-frame compression to optimize storage.
   • Essential for streaming and storage of video content, balancing quality and bandwidth usage.

9. Compression ratios and trade-offs

   • Compression ratio measures the reduction in file size achieved through compression.
   • Higher compression ratios can lead to greater loss of quality, especially in lossy formats.
   • Understanding trade-offs is crucial for selecting the appropriate compression method for specific applications.

10. File formats and their associated compression methods

    • Different file formats utilize specific compression techniques tailored to their data types (e.g., PNG for lossless image compression, MP4 for lossy video compression).
    • Knowledge of file formats helps in choosing the right method for data storage and transmission.
    • Familiarity with these formats is essential for effective data management and application development in computer science.