Compression using Huffman and arithmetic codes.
Source coding removes redundancy: lossless (Huffman, arithmetic, LZ77/LZW, DEFLATE) and lossy (JPEG, MP3, H.264/H.265) with rate-distortion trade-offs. Entropy H=-Σp log₂p is the lower bound for lossless compression. Transform coding concentrates energy in few coefficients.
ZIP files use DEFLATE (LZ77 + Huffman).
Source coding removes redundancy from data before transmission, which saves bandwidth and storage.
Compression using Huffman and arithmetic codes.
Source coding removes redundancy from data before transmission, which saves bandwidth and storage.
The average information per symbol, which sets the lower limit on the average code length for lossless compression.
It assigns shorter codes to more frequent symbols and longer ones to rare symbols, giving near-optimal lossless compression.
Typical applications include compressing a text file with Huffman coding, reducing the size of a sensor log before sending it over a slow link and using arithmetic coding in an image compressor.
Study "Bit error rate" first (topic 82 of 101 in Signals, Control & Comms). It covers the groundwork this topic builds on.
Prefix codes. Entropy. Quantisation in lossy coding.
ZIP files use DEFLATE (LZ77 + Huffman).
The next topic is "Channel coding", which builds directly on this one.
It is classed as Advanced in Signals, Control & Comms (topic 83 of 101). It is an advanced topic. It assumes you are comfortable with the intermediate topics in Signals, Control & Comms.
Want to practise? Open the interactive course for quick checks, quizzes, simulations and progress tracking.