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Compression algorithms can reduce file sizes without losing quality by consolidating repeated data or replacing blocks of code with more compact representations, thereby reducing storage demands and improving data transmission rates.

Findings

Additional insights we found via CrashCourse

  1. Run-length encoding replaces strings of repeated values, such as ten zeroes in a row, with the value and its count, which uses fewer bits (e.g., 0000000000 → 10x0).

  2. Dictionary coding finds nonconsecutive sequences of data that repeat throughout a file's code and replaces them with a shorter code, which can be translated back to its original form using a "dictionary" that stores all the substitutions (e.g., ABBBAABBBABBBABBBAAABBB → 0100101010001, where BBB = 0).

  3. Huffman coding, which forms the basis of ZIP, JPEG, and other file formats, counts how often segments of computer code, such as those corresponding to specific characters, appear in a file and replaces each segment with a new code, where those appearing most often get the shortest replacement, thus ensuring efficient file compression.

  4. Lossy compression algorithms remove certain segments of a file's code that are considered unimportant, such as high-resolution details in background objects of an image, audio frequencies humans cannot hear, or unchanged pixels between frames of a video.

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