V. UnShrinking - Method 1
-------------------------
Shrinking is a Dynamic Ziv-Lempel-Welch compression algorithm
with partial clearing. The initial code size is 9 bits, and
the maximum code size is 13 bits. Shrinking differs from
conventional Dynamic Ziv-Lempel-Welch implementations in several
respects:
1) The code size is controlled by the compressor, and is not
automatically increased when codes larger than the current
code size are created (but not necessarily used). When
the decompressor encounters the code sequence 256
(decimal) followed by 1, it should increase the code size
read from the input stream to the next bit size. No
blocking of the codes is performed, so the next code at
the increased size should be read from the input stream
immediately after where the previous code at the smaller
bit size was read. Again, the decompressor should not
increase the code size used until the sequence 256,1 is
encountered.
2) When the table becomes full, total clearing is not
performed. Rather, when the compressor emits the code
sequence 256,2 (decimal), the decompressor should clear
all leaf nodes from the Ziv-Lempel tree, and continue to
use the current code size. The nodes that are cleared
from the Ziv-Lempel tree are then re-used, with the lowest
code value re-used first, and the highest code value
re-used last. The compressor can emit the sequence 256,2
at any time.
VI. Expanding - Methods 2-5
---------------------------
The Reducing algorithm is actually a combination of two
distinct algorithms. The first algorithm compresses repeated
byte sequences, and the second algorithm takes the compressed
stream from the first algorithm and applies a probabilistic
compression method.
The probabilistic compression stores an array of 'follower
sets' S(j), for j=0 to 255, corresponding to each possible
ASCII character. Each set contains between 0 and 32
characters, to be denoted as S(j)[0],...,S(j)[m], where m<32.
The sets are stored at the beginning of the data area for a
Reduced file, in reverse order, with S(255) first, and S(0)
last.
The sets are encoded as { N(j), S(j)[0],...,S(j)[N(j)-1] },
where N(j) is the size of set S(j). N(j) can be 0, in which
case the follower set for S(j) is empty. Each N(j) value is
encoded in 6 bits, followed by N(j) eight bit character values
corresponding to S(j)[0] to S(j)[N(j)-1] respectively. If
N(j) is 0, then no values for S(j) are stored, and the value
for N(j-1) immediately follows.
Immediately after the follower sets, is the compressed data
stream. The compressed data stream can be interpreted for the
probabilistic decompression as follows:
let Last-Character <- 0.
loop until done
if the follower set S(Last-Character) is empty then
read 8 bits from the input stream, and copy this
value to the output stream.
