The fact that file compression is mostly used to transport large files and, for example, MP3 files in order to pack as many songs as possible on your iPod, smartphone, computer, ..., but JPG / JPEG image format is also very common for images here, is also diligently compressed.
The lossless compression!
If you are using compression on large, high quality files, you will likely want to be able to decompress the files to their original state so that no audio or data quality is lost. Lossless compression is used here.
The lossy compression!
For creating just a few tracks to be heard on your iPod, smartphone, ... through the standard headphones, then the audio quality is not that important, and therefore lossy compression is used.
Lossy compression, because of the way it works, can reduce a file to a much smaller file size than the lossless one.
2.) Examine lossy compression in detail like a professional!
Lossless compression is a data compression technique that reduces the size of files or data without losing information or quality. With lossless compression, the original data can be perfectly reconstructed from the compressed data so that no data is lost. This is in contrast to lossy compression, which discards some data to achieve higher compression rates, but at the expense of lower quality.
Lossless compression algorithms work by finding and eliminating redundancy or repeating patterns in the data. Instead of storing the data in its original form, these algorithms encode the data in a more efficient way. Common lossless compression formats include ZIP, GZIP, PNG (for images), and FLAC (for audio).
Here's how lossless compression typically works:
Dictionary-based compression:
Lossless compression algorithms often use dictionaries or tables to store repeating sequences of data. When a repeated sequence occurs, the algorithm replaces it with a reference to the dictionary entry. This allows the data to be presented more compactly.
Run length encoding:
This method represents sequences of identical values with a single value followed by a count of how many times it repeats. For example, “AAAAABBB” can be compressed to “A5B3”.
Huffman coding:
Huffman coding assigns shorter codes to more frequently occurring data values and longer codes to less common ones. This optimizes the presentation of the data and reduces the overall size.
Burrows-Wheeler Transform (BWT):
BWT rearranges the characters in the data to group similar characters. This transformation facilitates the effective application of other compression techniques.
Lempel-Ziv-Welch (LZW):
LZW is a dictionary-based algorithm commonly used in GIF and the older versions of the ZIP format. During data processing, a dictionary of patterns in the data is created, replacing patterns with dictionary references.
Lossless compression is used in various applications where data integrity is critical, such as: E.g., archiving files, transferring data over unreliable channels, and reducing storage requirements without sacrificing data quality. However, it may not achieve compression rates as high as lossy compression techniques, which sacrifice some data quality for greater compression.
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