Compression

Introduction to Compression

  • Compression refers to the process of reducing file size or the amount of data storage space a file requires.
  • This process is vital in optimising data storage and improving data transmission speed.
  • Compression can be applied to various forms of data, such as images, audio, video and text files.

Types of Compression

  • There are two types of compression: lossless and lossy compression.
  • Lossless compression ensures that all data can be fully restored to its original form after being compressed. This is typically used for text, software programs and some types of images where total accuracy is required.
  • In lossy compression, some data is permanently lost during the compression process. This is most commonly used for audio, video, and image files where a small loss in quality is acceptable.

Advantages and Disadvantages of Compression

  • The main advantages of data compression include: Less memory storage required, faster file transfer speeds, and cost savings (due to reduced need for storage equipment or bandwidth).
  • Some disadvantages may include the time it takes to compress and decompress the data, potential loss of data quality in lossy compression, and the need for specific software to decompress the data.

Factors Affecting Compression

  • The effectiveness of compression is influenced by several factors including the type of data, the compression algorithm used, and the acceptable data quality level after compression.
  • Different types of data yield different compression ratios. For instance, compressing an image file may result in a higher compression ratio than compressing a text file.
  • Moreover, different compression algorithms are more effective for specific types of data. For example, the Run Length Encoding (RLE) method is generally more suitable for text and monochromatic bitmap images than for full-colour photos.

Understanding Compression Algorithms

  • A compression algorithm is the specific method used to reduce the size of data.
  • These algorithms work by eliminating redundancy and identifying patterns within the data.
  • Common compression algorithms include Run Length Encoding (RLE), Huffman coding, and Dictionary based methods (like LZW).
  • Understanding the principles of these common compression algorithms has direct relevance to various aspects of computer science, including data management and security.