dsi-bitstream

Crates.iodsi-bitstream
lib.rsdsi-bitstream
version0.4.2
sourcesrc
created_at2024-01-14 10:50:02.451133
updated_at2024-04-07 19:09:07.49997
descriptionA Rust implementation of read/write bit streams supporting several types of instantaneous codes
homepage
repositoryhttps://github.com/vigna/dsi-bitstream-rs/
max_upload_size
id1099350
size11,595,420
Sebastiano Vigna (vigna)

documentation

README

dsi-bitstream

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A Rust implementation of bit streams supporting several types of instantaneous codes for compression.

This library mimics the behavior of the analogous classes in the DSI Utilities, but it aims at being much more flexible and (hopefully) efficient.

The two main traits are BitRead and BitWrite, with which are associated two main implementations BufBitReader and BufBitWriter. Additional traits make it possible to read and write instantaneous codes, like the exponential Golomb codes used in H.264 (MPEG-4) and H.265.

# fn main() -> Result<(), Box<dyn std::error::Error>> {
use dsi_bitstream::prelude::*;
// To write a bit stream, we need first a WordWrite around an output backend
// (in this case, a vector), which is word-based for efficiency.
// It could be a file, etc. 
let mut word_write = MemWordWriterVec::new(Vec::<u64>::new());
// Let us create a little-endian bit writer. The write word size will be inferred.
let mut writer = BufBitWriter::<LE, _>::new(word_write);
// Write 0 using 10 bits
writer.write_bits(0, 10)?;
// Write 1 in unary code
writer.write_unary(0)?;
// Write 2 in γ code
writer.write_gamma(1)?;
// Write 3 in δ code
writer.write_delta(2)?;
writer.flush();

// Let's recover the data
let data = writer.into_inner()?.into_inner();

// Reading back the data is similar, but since a reader has a bit buffer
// twice as large as the read word size, it is more efficient to use a 
// u32 as read word, so we need to transmute the data.
let data = unsafe { std::mem::transmute::<_, Vec<u32>>(data) };
let mut reader = BufBitReader::<LE, _>::new(MemWordReader::new(data));
assert_eq!(reader.read_bits(10)?, 0);
assert_eq!(reader.read_unary()?, 0);
assert_eq!(reader.read_gamma()?, 1);
assert_eq!(reader.read_delta()?, 2);

# Ok(())
# }

In this case, the backend is already word-based, but if you have a byte-based backend such as a file WordAdapter can be used to adapt it to a word-based backend.

You can also use references to backends instead of owned values, but this approach is less efficient:

# fn main() -> Result<(), Box<dyn std::error::Error>> {
use dsi_bitstream::prelude::*;
let mut data = Vec::<u64>::new();
let mut word_write = MemWordWriterVec::new(&mut data);
let mut writer = BufBitWriter::<LE, _>::new(word_write);
writer.write_bits(0, 10)?;
writer.write_unary(0)?;
writer.write_gamma(1)?;
writer.write_delta(2)?;
writer.flush();
drop(writer); // We must drop the writer release the borrow on data

let data = unsafe { std::mem::transmute::<_, Vec<u32>>(data) };
let mut reader = BufBitReader::<LE, _>::new(MemWordReader::new(&data));
assert_eq!(reader.read_bits(10)?, 0);
assert_eq!(reader.read_unary()?, 0);
assert_eq!(reader.read_gamma()?, 1);
assert_eq!(reader.read_delta()?, 2);
# Ok(())
# }

Please read the documentation of the traits module and the impls module for more details.

Options

There are a few options to modify the behavior of the bit read/write traits:

  • Endianness can be selected using the BE or LE types as the first parameter. The native endianness is usually the best choice, albeit sometimes the lack of some low-level instructions (first bit set, last bit etc, etc.) may make the non-native endianness more efficient.
  • Data is read from or written to the backend one word at a time, and the size of the word can be selected using the second parameter, but it must match the word size of the backend, so it is usually inferred. Currently, we suggest usize for writing and a type that is half of usize for reading.

More in-depth (and much more complicated) tuning can be obtained by modifying the default values for the parameters of instantaneous codes. Methods reading or writing instantaneous codes are defined in supporting traits and usually have const type parameters, in particular, whether to use decoding tables or not (e.g., GammaReadParam::read_gamma_param). Such traits are implemented for BitRead/BitWrite. The only exception is unary code, which is implemented by BitRead::read_unary and BitWrite::write_unary.

However, there are traits with non-parametric methods (e.g., GammaRead::read_gamma) that are the standard entry points for the user. These traits are implemented for BufBitReader/BufBitWriter depending on a selector type implementing ReadParams/WriteParams, respectively. The default value for the parameter is DefaultReadParams/DefaultWriteParams, which uses choices we tested on several platforms and that we believe are good defaults, but by passing a different implementation of ReadParams/WriteParams you can change the default behavior. See params for more details.

Finally, if you choose to use tables, the size of the tables is hardwired in the source code (in particular, in the files *_tables.rs in the codes source directory) and can be changed only by regenerating the tables using the script gen_code_tables.py in the python directory. You will need to modify the values hardwired at the end of the script.

Benchmarks

To evaluate the performance on your hardware you can run the benchmarks in the benchmarks directory, which test the speed of read/write operations under several combinations of parameters. Please refer to the crate documentation therein. The svg directory contains reference results of these benchmarks of a few architectures.

Testing

Besides unit tests, we provide zipped precomputed corpora generated by fuzzing. You can run the tests on the zipped precomputed corpora by enabling the fuzz feature:

cargo test --features fuzz

When the feature is enabled, tests will be also run on local corpora found in the top-level fuzz directory, if any are present.

Acknowledgments

This software has been partially supported by project SERICS (PE00000014) under the NRRP MUR program funded by the EU - NGEU, and by project ANR COREGRAPHIE, grant ANR-20-CE23-0002 of the French Agence Nationale de la Recherche.

Commit count: 416

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