Buffer wordpresscom integrations6/12/2023 The actual rANS bitstream needs to be encoded in reverse though (we prefer our decoders to consume data in increasing address order). the LZ parse and the statistics of the symbols emitted, which makes encoding harder and somewhat slower, but such is life. Static models are trickier to encode for because there are interdependencies between e.g. Kraken, Mermaid and Leviathan (but not Selkie, which is a byte-aligned LZ with no entropy coding) support multiple choices of entropy coders but they’re all based on static models.īitKnits use of a semi-adaptive model makes it easy to do the modelling and most of the encoding in a single front-to-back pass. Our later codecs jettisoned this part of the design Oodle LZNA (as the name suggests, a LZMA-derived design) used multi-symbol rANS but with full adaptation after every symbol. It was significantly faster than LZMA/CABAC/etc.-style “update after every symbol” fully adaptive models, but with appreciably worse compression, and slower to decode than a fully static model would’ve been (which would’ve also enabled tANS usage). The particular model it ended up with was a compromise to hit its speed targets, and its update mechanism wasn’t great. Semi-adaptive meaning that they change over time, but not after every symbol model updates are batched and the model remains static in between, which lets it amortize update cost and build some auxiliary data structures to accelerate decoding. This is somewhat expensive to maintain in the encoder/decoder and mostly a waste for ASCII text and text-based formats, but turns out to be very useful for the structured binary data that was its intended use case.įor entropy coding, it uses a 2-way interleaved rANS coder with 32-bit state that emits 16-bit words at a time, and 15-bit “semi-adaptive” (aka deferred summation) multi-symbol (i.e. Like LZX and then LZMA, it keeps a history of recent match offsets that can be cheaply reused BK was designed mainly for Granny files, which are chock full of fixed-size records with highly structured offsets, so unlike LZMAs LRU history of 3 recent match offsets, it keeps 7. Basic overviewīitKnit (I’ll just write BK from here on out) is basically your usual LZ77 with entropy coding backend. So I’ll keep it brief and focus mainly on the parts of it that actually got successfully used elsewhere. In Oodle it was quickly superseded by the “oceanic cryptozoology” (first Kraken, then later Mermaid/Selkie and Leviathan) codecs, so BitKnit the actual codec is just a historical curiosity these days (and deprecated in current Oodle versions), but it was our first released code to use several ideas that later ended up in other Oodle codecs, and might be of general interest. The codec did end up in Granny and later a small variation (with slightly different bitstream to work within the different container) in Oodle 2.1.2. Let’s just say I wish I hadn’t posted this in its original form, but I did, and I’d rather make the corrections publicly now than pretend it didn’t happen.īitKnit is a LZ77+RANS lossless general-purpose compressor that was designed 8 years ago (1.0 release on May 20, 2015) to replace the aging and very slow to encode LZ77+arithmetic coding codec built into Granny 3D, and also worked – somewhat unintentionally – as a LZ77/entropy coding test vehicle. These have been corrected in this version and I’ve marked the places where I was wrong. UPDATE May 7, 2023: I wrote this post yesterday somewhat in a huff (for reasons not worth going into) and the original post contained several inaccuracies.
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