How to Efficiently Store Huffman Trees for Chunked File Processing?

Efficient Huffman Tree Storage

When designing a Huffman encoding or decoding tool, a crucial aspect is finding an efficient method to store the constructed Huffman tree within the output file. This efficiency becomes particularly important when dealing with incremental file processing.

Two Implementation Approaches

Two common implementation approaches exist:

1. Whole File Processing: The entire file is analyzed, creating a single frequency table for the whole document. The Huffman tree is built and stored once in the output. This method is less efficient for small files due to the limited gains in file size reduction.
2. Chunked File Processing: Data is processed in chunks of a fixed size. Frequency analysis, tree construction, and encoding occur for each chunk. This approach requires the Huffman tree to be stored before each chunk for proper decoding. Efficiency is crucial in this case to minimize overhead.

Efficient Tree Storage Method

To address the need for efficiency in the second approach, a method that stores the tree in a compact form is proposed:

Encoding:

• If a node is a leaf (no children), encode it as 1-bit N-bit character/byte.
• If not a leaf (has children), encode it as 0-bit. Encode both left and right child nodes recursively.

Decoding:

• Read a bit.
• If 1, read N bits and return a leaf node with the specified value.
• If 0, recursively decode the left and right child nodes and return a new node with no value.

Example

Consider the example sequence "AAAAAABCCCCCCDDEEEEE":

• Frequencies:

  • A: 6
  • B: 1
  • C: 6
  • D: 2
  • E: 5
• Tree Size: 49 bits
• Encoded Data Size: 43 bits
• Total Output: 92 bits (12 bytes)

This tree storage method efficiently represents the Huffman tree in the output file, reducing the overhead compared to storing the actual frequencies.

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