avkodingsalgoritmer

Avkodingsalgoritmer, or decoding algorithms, are procedures used to reconstruct the original information from data that has been encoded for transmission or storage. In coding theory, decoding aims to recover the message from encodings that may have been corrupted by noise. Decoding can target channel coding (error correction) or source coding (data compression), though many practical systems treat both aspects as part of data recovery.

In practice, decoding methods balance accuracy, complexity, and latency. Common approaches include maximum-likelihood decoding, which selects the most probable original codeword given the received data, and various suboptimal methods that are efficient enough for real-time use. Many modern systems rely on soft-decision information, where the decoder uses probabilistic estimates rather than hard bit values.

Notable families and algorithms include: convolutional codes decoded with the Viterbi algorithm, which finds the most likely path through a trellis; turbo codes and LDPC codes, which use iterative decoding and soft information with BCJR or belief-propagation (sum-product) algorithms to approach channel capacity. For Reed-Solomon and related codes, decoding often employs the Berlekamp–Massey algorithm, with Forney’s formula to determine error magnitudes. Reed-Solomon codes are widely used in data storage and communication systems, such as CDs, DVDs, QR codes, and certain broadcast channels.

Source coding decoders reverse data compression and include algorithms for Huffman coding, arithmetic coding, and Lempel–Ziv variants. These decoding tasks focus on recovering the original bitstream or symbols, rather than correcting transmission errors.

Implementation considerations include hardware versus software realizations, latency constraints, memory usage, and the trade-offs between hard-decision and soft-decision decoding.