> mtf | move | front <
// Move-to-Front - Dynamic list reordering for improved compression
Self-Adaptive
Automatically adapts to local patterns in data.
Exploits Locality
Recent symbols get smaller indices for better compression.
Part of bzip2
Used after BWT in bzip2 compression pipeline.
>> technical info
How MTF Works:
MTF maintains a list of all possible symbols. When encoding a symbol, output its current position in the list, then move it to the front. This gives recently used symbols smaller indices, which compress better with subsequent entropy coding.
Encoding Example:
Text: "banana" Initial list: [a,b,c,d,...] 'b': index 1, move b→front [b,a,c,d,...] 'a': index 1, move a→front [a,b,c,d,...] 'n': index 13, move n→front [n,a,b,c,...] 'a': index 1, move a→front [a,n,b,c,...] 'n': index 1, move n→front [n,a,b,c,...] 'a': index 1, move a→front [a,n,b,c,...] Output: [1,1,13,1,1,1]
Why Use MTF:
- >Improves local redundancy
- >Works with BWT
- >Simple implementation
- >Reversible transform
- >Adaptive to patterns
>> frequently asked questions
What is Move-to-Front encoding?
MTF is a data transformation algorithm that encodes each symbol as its index in a dynamically updated list. After encoding each symbol, it's moved to the front of the list, giving frequently used symbols smaller indices.
Why use MTF with BWT?
BWT clusters similar contexts together, creating local repetitions. MTF then transforms these repetitions into small numbers (many 0s and 1s), which compress very well with subsequent Run-Length or Huffman encoding.
How does the list work?
The list starts with all 256 possible byte values in order. As symbols are encoded, they move to the front. Recently used symbols stay near the front with small indices, while unused symbols drift to larger indices.
MTF vs other transforms?
MTF is specifically designed to work after BWT. It's not effective alone but excels at converting BWT's output into a form that compresses well. It's simpler than arithmetic coding but less optimal.