Adaptive coding refers to variants of entropy encoding methods of lossless data compression. They are particularly suited to streaming data, as they adapt to localized changes in the characteristics of the data, and don't require a first pass over the data to calculate a probability model. The cost paid for these advantages is that the encoder and decoder must be more complex to keep their states synchronized, and more computational power is needed to keep adapting the encoder/decoder state.
Almost all data compression methods involve the use of a model, a prediction of the composition of the data. When the data matches the prediction made by the model, the encoder can usually transmit the content of the data at a lower information cost, by making reference to the model. While sometimes the model is implicit in the compression method (for instance, in run_length encoding), in most methods it is separate, and because both the encoder and the decoder need to use the model, it must be transmitted with the data.
In adaptive coding, the encoder and decoder are instead equipped with identical rules about how they will alter their models in response to the actual content of the data, and otherwise start with a blank slate, meaning that no initial model needs to be transmitted. As the data is transmitted, both encoder and decoder adapt their models, so that unless the character of the data changes radically, the model becomes better_adapted to the data it's handling and compresses it more efficiently.
Adaptive Huffman coding is an adaptivecoding technique based on Huffman coding, building the code as the symbols are being transmitted, having no initial knowledge of source distribution, that allows one-pass encoding and adaptation to changing conditions in data.
Code is represented as a tree structure in which every node has a corresponding weight and a unique number.
Paul E. Black, adaptive Huffman coding at the NIST Dictionary of Algorithms and Data Structures.
Adaptivecoding refers to variants of entropy encoding methods of lossless data compression.
While sometimes the model is implicit in the compression method (for instance, in run-length encoding), in most methods it is separate, and because both the encoder and the decoder need to use the model, it must be transmitted with the data.
In adaptivecoding, the encoder and decoder are instead equipped with identical rules about how they will alter their models in response to the actual content of the data, and otherwise start with a blank slate, meaning that no initial model needs to be transmitted.
Feeds |
Contact