The code is for experimenting adaptive huffman compression with some raw data images.
(ie. Baboon and lena in raw,halftone and binary color)
- Visual Studio Code
- Poetry 1.0.10 or later
- Clone this repository to local
- Open the root directory.
cd adaptive_huffman_coding- Install the dependencies with Poetry.
poetry install --no-root- Run main.py
python3 main.pyUse a DAG, which has 2 children and parent pointer for every node, to implement Huffman tree. The code of a node is generated when we search for it (Tree.search() in class Tree of tree.py), and thus the code for every node will not be updated when exchanging or appending. Furthermore, we use an array (self.all_nodes in class AdaptiveHuffman of ada_huffman.py) to keep track of each node within the tree. By doing this, we could search a node within the tree iteratively instead of recursively traversal.
Baboon
| Image(256*256) | Entropy(bit) | Before(byte) | After(byte) -without header | Header(byte) | Compression Rate |
|---|---|---|---|---|---|
| baboon_b | 0.96909 | 65536 | 11446 | 4 | 82.53% |
| baboon_halftone | 0.96136 | 65536 | 11347 | 4 | 82.68% |
| baboon_raw | 7.24065 | 65536 | 59828 | 146 | 8.71% |
Lena
| Image(256*256) | Entropy(bit) | Before(byte) | After(byte) -without header | Header(byte) | Compression Rate |
|---|---|---|---|---|---|
| lena_b | 0.96968 | 65536 | 11454 | 4 | 82.52% |
| baboon_halftone | 0.83681 | 65536 | 10380 | 4 | 84.16% |
| baboon_raw | 7.59536 | 65536 | 62832 | 214 | 4.13% |
forked from https://github.com/seanwu1105/adaptive-huffman-coding
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