mirror of
https://github.com/xcat2/xNBA.git
synced 2025-10-31 11:22:29 +00:00
59 lines
3.7 KiB
Plaintext
59 lines
3.7 KiB
Plaintext
The compressor achieves an average compression rate of 60% of the
|
|
original size which is on par with "gzip". It seems that you cannot do
|
|
much better for compressing compiled binaries. This means that the
|
|
break even point for using compressed images is reached, once the
|
|
uncompressed size approaches 1.5kB. We can stuff more than 12kB into
|
|
an 8kB EPROM and more than 25kB into an 16kB EPROM. As there is only
|
|
32kB of RAM for both the uncompressed image and its BSS area, this
|
|
means that 32kB EPROMs will hardly ever be required.
|
|
|
|
The compression algorithm uses a 4kB ring buffer for buffering the
|
|
uncompressed data. Before compression starts, the ring buffer is
|
|
filled with spaces (ASCII character 0x20). The algorithm tries to
|
|
find repeated input sequences of a maximum length of 60 bytes. All
|
|
256 different input bytes plus the 58 (60 minus a threshold of 2)
|
|
possible repeat lengths form a set of 314 symbols. These symbols are
|
|
adaptively Huffman encoded. The algorithm starts out with a Huffmann
|
|
tree that assigns equal code lengths to each of the 314 symbols
|
|
(slightly favoring the repeat symbols over symbols for regular input
|
|
characters), but it will be changed whenever the frequency of any of
|
|
the symbols changes. Frequency counts are kept in 16bit words until
|
|
the total number of compressed codes totals 2^15. Then, all frequency
|
|
counts will be halfed (rounding to the bigger number). For unrepeated
|
|
characters (symbols 0..255) the Huffman code is written to the output
|
|
stream. For repeated characters the Huffmann code, which denotes the
|
|
length of the repeated character sequence, is written out and then the
|
|
index in the ring buffer is computed. From this index, the algorithm
|
|
computes the offset relative to the current index into the ring
|
|
buffer. Thus, for typical input data, one would expect that short to
|
|
medium range offsets are more frequent than extremely short or medium
|
|
range to long range offsets. Thus the 12bit (for a 4kB buffer) offset
|
|
value is statically Huffman encoded using a precomputed Huffman tree
|
|
that favors those offset values that are deemed to be more
|
|
frequent. The Huffman encoded offset is written to the output data
|
|
stream, directly following the code that determines the length of
|
|
repeated characters.
|
|
|
|
This algorithm, as implemented in the C example code, looks very good
|
|
and its operating parameters are already well optimized. This also
|
|
explains why it achieves compression ratios comparable with
|
|
"gzip". Depending on the input data, it sometimes excells considerably
|
|
beyond what "gzip -9" does, but this phenomenon does not appear to be
|
|
typical. There are some flaws with the algorithm, such as the limited
|
|
buffer sizes, the adaptive Huffman tree which takes very long to
|
|
change, if the input characters experience a sudden change in
|
|
distribution, and the static Huffman tree for encoding offsets into
|
|
the buffer. The slow changes of the adaptive Huffman tree are
|
|
partially counteracted by artifically keeping a 16bit precision for
|
|
the frequency counts, but this does not come into play until 32kB of
|
|
compressed data is output, so it does not have any impact on our use
|
|
for "etherboot", because the BOOT Prom does not support uncompressed
|
|
data of more then 32kB (c.f. doc/spec.doc).
|
|
|
|
Nonetheless, these problems do not seem to affect compression of
|
|
compiled programs very much. Mixing object code with English text,
|
|
would not work too well though, and the algorithm should be reset in
|
|
between. Actually, we might gain a little improvement, if text and
|
|
data segments were compressed individually, but I have not
|
|
experimented with this option, yet.
|