Optimizations

There are some techniques we can use to optimize Huffman coding for better performance and compression efficiency.

Using Canonical Code Sets

Canonical Huffman codes use a special property to allow the code table itself to be stored and transmitted more efficiently.

Rather than transmitting the full tree structure to communicate the codes, canonical codes allow the table to be represented as a simple array. The codes are constructed in a way such that the table can be rebuilt based just on the lengths of each code.

This allows for much more compact storage and transmission of the code set needed for decoding. The decoder can rebuild the Huffman tree from the length information rather than requiring the full tree.

Strategies for Tree Rebuilding

In many applications, the input data statistics may change over time. As the frequencies evolve, the optimal Huffman tree also changes.

Adaptive Huffman coding approaches address this by periodically rebuilding the tree to reflect the updated frequencies. This allows the compression to continuously adapt for maximum efficiency.

Some common strategies include:

• Rebuilding after a certain number of inputs or time interval
• Rebuilding when the compression ratio falls below a threshold
• Incrementally updating the tree with new frequencies rather than full rebuilds

The frequency of tree rebuilds involves tradeoffs between compression optimality and computational overhead. More frequent rebuilding provides better compression but requires more computation.