Streaming, network-aware image codecs for scientific imaging (prototype: JPEG XL)
Project description
opencodecs
Native, parallel-decode codecs for scientific imaging. One unified Codec / Reader / Writer API across compression streams, single images, multi-frame stacks, and chunked containers.
Built for fast modern storage (NVMe, 10 G NAS) where the bottleneck is codec dispatch and per-tile parallelism, not raw I/O bandwidth. Native implementations of every codec — no runtime delegation to imagecodecs — though we use its excellent test suite as a parity reference.
import opencodecs as oc
arr = oc.read("scan.czi") # auto-detect by extension
arr = oc.read("photo.jxl")
arr = oc.read(blob) # auto-detect by magic bytes
oc.write("out.jxl", arr, lossless=True)
oc.write("out.zst", b"...payload...", level=10)
# Streaming reader for multi-frame / chunked formats
with oc.get_codec("czi").open(path) as r:
print(r.shape, r.dtype, r.n_frames)
for tile in r: # iter_frames
...
tile5 = r[5] # random access
# Discovery
oc.list_codecs() # capability table
oc.has_codec("avif")
Codec capability matrix
All codecs below are native implementations linking against system or vendored C libraries. Build skips cleanly when an optional system library is missing — see INSTALL.md.
Compression (bytes → bytes)
| Codec | Encode | Decode | Backing library | Extension |
|---|---|---|---|---|
zstd |
✓ | ✓ | system libzstd | .zst |
lz4 |
✓ | ✓ | system liblz4 (frame) | .lz4 |
brotli |
✓ | ✓ | system libbrotli | .br |
blosc2 |
✓ | ✓ | source-built c-blosc2 2.23 | .b2 |
deflate |
✓ | ✓ | libdeflate / zlib-ng / zlib (auto-selected at build time) | .zlib |
gzip |
✓ | ✓ | stdlib gzip | .gz |
none |
✓ | ✓ | identity (filter-chain placeholder) | — |
bz2 |
✓ | ✓ | stdlib bz2 | .bz2 |
lzma |
✓ | ✓ | stdlib lzma | .xz |
snappy |
✓ | ✓ | system snappy | .sz |
bitshuffle |
✓ | ✓ | vendored bitshuffle (filter) | — |
bitshuffle is a filter, not a stand-alone compressor: bit-level
transpose that radically improves LZ77 ratios on typed numerical data.
Output size equals input size; pair with zstd / lz4. Aliases:
bshuf.
deflate aliases: zlib, zlibng. Pass backend="isal" to opt into
Intel ISA-L's igzip (~4× faster encode on x86_64; opt-in because
output is ~19% bigger). The default backend is auto-selected at build
time: libdeflate when present (fastest at default level), else
zlib-ng-compat, else the stdlib zlib.
Scientific / numerical-array codecs (ndarray ↔ bytes, self-describing)
These four codecs target typed multidimensional arrays rather than
images or raw bytes. The encoded blob carries shape and dtype in its
header, so decode(blob) reconstructs the full ndarray without
out-of-band metadata.
| Codec | Encode | Decode | Lossless | Lossy modes | Backing library | Extension |
|---|---|---|---|---|---|---|
b2nd |
✓ | ✓ | ✓ | — | system c-blosc2 (NDim API) | .b2nd |
aec |
✓ | ✓ | ✓ | — | system libaec (CCSDS 121.0-B-2) | .aec |
lerc |
✓ | ✓ | ✓ | max_z_error |
system liblerc (Esri) | .lerc |
zfp |
✓ | ✓ | ✓ (reversible) | rate / precision / accuracy | system libzfp | .zfp |
sz3 |
✓ | ✓ | — | abs / rel / psnr / norm | source-built SZ3 | .sz3 |
pcodec |
✓ | ✓ | ✓ | — | source-built pcodec (Rust) | .pco |
Quick guidance:
pcodec— modern lossless numerical compressor; often beatszstdby 1.5–3× on float / int arrays without a pre-filter.b2nd— c-blosc2's multidim layer with shuffle/bitshuffle filters built in; great when you already use blosc2 elsewhere.aec— entropy coder used by NetCDF-4 SZIP; lossless integers.lerc— fast (lossy or lossless) raster codec used in Cloud-Optimized GeoTIFF, Esri MRF.zfp— fast 1D-4D float / int compression with multiple lossy modes (predictable size, accuracy, or precision).sz3— error-bounded prediction-based scientific compressor; often beatszfpat the same error budget on simulation snapshots. Float only (the SZ3 v3 C API doesn't dispatch integer types).
Single-image codecs
| Codec | Encode | Decode | Color | Backing library | Extension |
|---|---|---|---|---|---|
qoi |
✓ | ✓ | RGB / RGBA | vendored qoi.h | .qoi |
bmp |
✓ | ✓ | gray / RGB / RGBA | pure Python+numpy | .bmp, .dib |
png |
✓ | ✓ | gray / RGB / RGBA, 8/16-bit | vendored libspng + libdeflate | .png |
jpeg |
✓ | ✓ | gray / RGB | libjpeg-turbo (TJ v3) | .jpg, .jpeg |
mozjpeg |
✓ | ✓ | gray / RGB, 8/12-bit | system mozjpeg (TJ v2) | .jpg |
webp |
✓ | ✓ | RGB / RGBA, lossy + lossless | system libwebp | .webp |
jpeg2k |
✓ | ✓ | gray / RGB / RGBA, 8/16-bit, lossless + lossy | OpenJPEG | .jp2, .j2k, .jpx, .jpc |
htj2k |
✓ | ✓ | gray / RGB / RGBA, 8/16-bit, lossless + lossy | system OpenJPH | .j2c |
jpegls |
✓ | ✓ | gray / RGB / RGBA, 2-16 bit, lossless + near-lossless | system CharLS | .jls |
avif |
✓ | ✓ | RGB / RGBA, lossy + lossless (YUV444+identity) | libavif | .avif |
heif |
✓ | ✓ | RGB / RGBA, lossy (HEVC) | libheif (+ aomenc) | .heif, .heic |
jxl |
✓ | ✓ | gray / RGB / RGBA, P3, HDR, multi-frame | vendored libjxl 0.11.2 | .jxl |
bcdec |
— | ✓ | BC1-7 / DXT / BPTC GPU textures | vendored bcdec.h | .dds |
rgbe |
✓ | ✓ | float32 RGB HDR (Radiance) | vendored rgbe.c | .hdr |
ultrahdr |
✓ | ✓ | float16 / uint8 / uint16 RGBA HDR + SDR | system libultrahdr 1.4.x | .jpg (gainmap) |
htj2k is JPEG-2000 Part 15 (High-Throughput) — same DWT front end
as classic JPEG-2000 but ~10-20× faster entropy coding. Used by
modern DICOM and remote-sensing pipelines.
jpegls (CharLS) is the lossless / near-lossless predictive JPEG
variant standardized as ISO/IEC 14495-1 — the dominant codec in
medical-imaging DICOM workflows.
mozjpeg is Mozilla's libjpeg-turbo fork; ~10-15% smaller files
than libjpeg-turbo at the same quality. Built only when MozJPEG is
on the system (keg-only on Homebrew so it doesn't collide with
plain libjpeg-turbo).
rgbe is the canonical Radiance HDR format — float32 RGB shared-
exponent encoding for high-dynamic-range photography and physically-
based rendering output. ultrahdr is the ISO 21496 gainmap-JPEG
format — Android Camera's default since A14 and what iOS 18+ reads
natively. Decode dtype controls the output: float16 returns linear
BT.2100 HDR; uint8 returns the SDR-tonemapped base JPEG.
Multi-frame / chunked formats
| Codec | Read | Write | Container | Notes |
|---|---|---|---|---|
jxl |
✓ | ✓ | ISO BMFF (frame index) | Streaming + parallel multi-frame decode |
czi |
✓ | ✓ | Zeiss ZISRAW | mmap + parallel zstd; metadata accessor; parallel bulk HTTP fetch via CziReader.from_http(max_workers=N) |
tiff |
✓ | ✓ | TIFF 6.0 + BigTIFF | Native reader + writer; tiled or strip; parallel encode; LZW encode; streaming write to unseekable sinks; EER cryo-EM dispatch |
ndtiff |
✓ | ✓ | Micro-Manager / Pycro-Manager NDTiff | Streaming writer; os.writev hot path; cross-platform (POSIX + Windows-NTFS-safe pre-allocation) |
hdf5 |
✓ | ✓ | HDF5 | Wraps h5py.Dataset. Remote HDF5 via open_remote_hdf5(url) — slices stream chunks over HTTP Range with one-shot parallel prefetch |
eer |
✓ | — | Thermo Fisher EER (cryo-EM event-list) | Native bitstream decoder + TIFF compression-tag dispatch (codes 65000-65002) |
dicomweb |
✓ | — | WADO-RS HTTP frame retrieval | Multipart/related parser; transfer-syntax dispatch through opencodecs's codec layer (JPEG-LS / HTJ2K / JPEG-2000 / RLE / raw) |
fits |
✓ | — | FITS (astronomy) | Multi-HDU walk; BITPIX 8/16/32/64/-32/-64; BZERO unsigned-int trick; compressed images (RICE_1, GZIP_1, GZIP_2, HCOMPRESS_1, NOCOMPRESS) with per-tile ZSCALE/ZZERO quantization. HTTP-range friendly — opening a 50 GB cube reads kilobytes. |
TIFF writer specifics
from opencodecs._tiff_writer import TiffWriter
# Classic TIFF (<4 GiB)
with TiffWriter("out.tif") as w:
w.write_page(arr, tile=(256, 256), compression="zstd")
# BigTIFF (>4 GiB; magic=43, 64-bit offsets)
with TiffWriter("huge.tif", bigtiff=True) as w:
w.write_pyramid(levels, compression="zstd", subifds=True)
# COG-style streaming to an unseekable sink (pipe, S3 multipart, HTTP body)
with TiffWriter(sink, streaming=True) as w:
w.write_stream(pages, total_pages=N, tile=(256, 256), compression="zstd")
Supported encode-side compressions: none, deflate (libdeflate / zlib-ng / zlib auto-detect), zstd, LZW, JPEG, JPEG2000, WebP, JXL, LERC. Horizontal predictor on byte-stream codecs.
OME-TIFF metadata
from opencodecs._ome_xml import write_ome_tiff, Channel
write_ome_tiff(
"scan.ome.tif", arr_5d, axes="TCZYX",
physical_size_um=(0.108, 0.108, 0.5),
channels=[Channel(name="DAPI", emission_wavelength_nm=460),
Channel(name="GFP", emission_wavelength_nm=520)],
)
Round-trips through tifffile / Bio-Formats / QuPath. For schema
elements outside the 80%-case subset, hand-author OME-XML and pass
via TiffWriter's metadata= kwarg.
Remote HDF5
from opencodecs._hdf5_http import open_remote_hdf5, prefetch_hdf5_chunks
with open_remote_hdf5("https://bucket.s3.amazonaws.com/big.h5") as f:
prefetch_hdf5_chunks(f["img"], np.s_[:1024, :1024]) # 1 syscall, N HTTP
arr = f["img"][:1024, :1024] # all from cache
czi decodes types 0 (uncompressed) and 6 (ZSTDHDR) — the entire
modern Zen archive. JPEG-XR sub-blocks (rare in 2022+ output) raise
NotImplementedError. The reader exposes metadata_bytes and
metadata_xml as lazy zero-copy accessors.
zarr v3 codecs
opencodecs._zarr_codecs registers our compressors as zarr v3
BytesBytesCodecs:
import zarr
from opencodecs._zarr_codecs import OcZstd, OcLz4, OcBlosc2, OcBrotli, OcDeflate
z = zarr.create_array(
store=..., shape=..., dtype=..., chunks=...,
compressors=[OcZstd(level=10)],
zarr_format=3,
)
Performance
Headline numbers from the latest bench run (bench/run_benchmarks.py --fast, macOS M1 Ultra, vs imagecodecs / tifffile / ndstorage):
| Workload | opencodecs | reference | ratio |
|---|---|---|---|
tiff_random_tile_read |
0.70 ms | 7.71 ms (tifffile) | 11× |
tiff_pyramid_crop_from_fullres |
0.47 ms | 8.60 ms | 18× |
ndtiff_index_parse_synthetic_10k |
4.61 ms | 28.0 ms (ndstorage) | 6.1× |
h2h_jxl_4mp_rgb (encode) |
130 ms | 3153 ms (imagecodecs) | 24× |
h2h_blosc2_10mb |
4.63 ms | 54.8 ms | 12× |
h2h_deflate_10mb (encode) |
109 ms | 296 ms | 2.7× |
h2h_png_4mp_rgb (encode) |
142 ms | 281 ms | 2.0× |
h2h_png_kodak_photo (encode) |
19 ms | 58 ms | 3.1× |
h2h_png_filterbound_u16 (encode) |
2.0 ms | 3.7 ms | 1.8× |
tiff_write_1gb |
89 ms | 91 ms | parity, +14% on Windows |
ndtiff_write_1gb (raw 800 MB) |
159 ms | 154 ms | parity (1.04× on macOS, 2.4× on Windows after NTFS-friendly pre-alloc) |
The PNG encode wins above stack two independent improvements:
the libdeflate IDAT accumulator (already shipped) collapses
zlib's per-scanline deflate() loop into a single one-shot call,
and a per-filter split of libspng's filter_sum hot path lets
the compiler autovectorize each branch into NEON/SSE — together
they make every PNG-encode workload 1.5–3.1× faster than
imagecodecs.
Remote-fetch workloads benefit from read_many (one batched HTTP
fan-out + Range coalescing) — on a loopback Range-supporting server,
1024-chunk HDF5 slices land in 7 HTTP requests instead of 1010 (a
~50× request-count reduction; on real-network RTT this translates
to 8× wall-clock).
Scientific microscopy CZI (66 MB, 14 sub-blocks of 2000×2000 uint16, ZSTDHDR), single-file warm cache:
| Reader | Mac M3 | Threadripper x86_64 |
|---|---|---|
| czifile (Python ref) | 148 ms | 414 ms |
| aicspylibczi (C++) | 17 ms | 140 ms |
| opencodecs | 15 ms | 46 ms |
See docs/io_patterns.md for the lessons learned about coalesced I/O, mmap vs pread, persistent thread pools, and where parallelism actually pays off. The deflate path is libdeflate when available → zlib-ng-compat → stdlib zlib, auto-detected at build time.
Public API
Top-level dispatch
oc.read(src, *, format=None, **opts) -> ndarray | bytes
oc.write(dest, data, *, format=None, **opts) -> bytes | None
oc.codec_for_path(path) -> Codec | None
oc.codec_for_bytes(head) -> Codec | None
src and dest accept paths, file-like objects, bytes, and
memoryview / mmap slices (zero-copy through the codec).
Codec registry
oc.list_codecs() -> list[Codec]
oc.has_codec(name_or_alias) -> bool
oc.get_codec(name_or_alias) -> Codec
Codec interface
Each codec exposes:
codec.name # "czi"
codec.file_extensions # (".czi",)
codec.has_native # True for everything we ship
codec.can_encode / codec.can_decode
codec.multi_frame / codec.chunked / codec.streaming_decode / codec.parallel_decode
codec.supported_dtypes / codec.supports_color
codec.signature(head_bytes) -> bool
codec.encode(data, *, dest=None, **opts) -> bytes | None
codec.decode(src, **opts) -> ndarray | bytes
codec.open(src, **opts) -> Reader # multi-frame / chunked
Reader interface (multi-frame / chunked)
reader.shape # (n_frames, *frame_shape)
reader.dtype
reader.n_frames
reader.is_chunked # True if [idx] random access works
reader.iter_frames()
reader.read() # full eager decode
reader[idx] # random access (chunked formats only)
CZI reader additionally exposes:
reader.entries # list[CziSubBlockEntry] — sub-block metadata
reader.metadata_bytes # raw UTF-8 bytes (lazy + cached)
reader.metadata_xml # decoded str (lazy + cached)
reader.subblock_metadata_bytes(i)
HDF5 reader additionally exposes:
reader.dataset_names # all numeric datasets in the file
reader.select(name) # switch to a different dataset
Install
See INSTALL.md for system dependencies per platform and build instructions, and docs/publishing.md for the wheel-publishing pipeline (TestPyPI / PyPI via Trusted Publishing). Short version:
# macOS
brew install jpeg-turbo webp libavif libheif openjpeg libtiff hdf5 c-blosc2 \
charls openjph libdeflate zlib-ng-compat
# Ubuntu / Debian
sudo apt install -y libturbojpeg0-dev libwebp-dev libavif-dev libheif-dev \
libopenjp2-7-dev libblosc2-dev libcharls-dev \
liblz4-dev libspng-dev libtiff-dev libhdf5-dev \
libdeflate-dev libopenjph-dev zlib1g-dev
# Build
cd opencodecs
pip install -e .
# or
python setup.py build_ext --inplace
The build skips cleanly for any system library that's missing — useful extensions still build, missing ones print a one-line notice.
libjxl 0.11.2 is vendored via bench/build_libjxl.sh (auto-builds + caches
to ~/Library/Caches/opencodecs/libjxl/ on Mac, ~/.cache/opencodecs/libjxl/
on Linux). See INSTALL.md for the rationale (Homebrew/apt builds are
0.5-0.7× slower than a tuned -O3 + LTO build).
Status
- Core API stable; 1066 tests passing (Mac M1 Ultra + Linux + Windows VM)
- Native readers + writers for the common scientific containers (TIFF, BigTIFF, OME-TIFF, CZI, NDTiff, HDF5, JXL)
- Cross-platform bench coverage: Mac arm64 (canonical), Windows 11 LTSC (libvirt VM), Linux x86_64 (Threadripper)
- Compression backend auto-detect (libdeflate → zlib-ng-compat → stdlib)
- Cloud I/O primitives (
HTTPDataSource.read_many, range coalescing, HTTP/1.1 keep-alive) wired into TIFF / HDF5 / DICOMweb / CZI readers tifffile_patchopt-in shim reroutes tifffile's codec dispatch through opencodecs for users who want only a partial swap
Deferred work (see docs/TODO_DEFERRED.md):
- SPERR (error-bounded lossy scientific compression) — CMake build needed
- Brunsli (lossless JPEG transcoder) — source build needed; no brew formula
- CCITT Fax3/Fax4 encode — legacy fax; zero scientific users
- JPEG-XR — abandoned format outside niche DICOM
- libspng
filter_sumSIMD — off the bench-tracked workload (h2h_png_4mp_rgbis at 1.14× already); filter-bound PNG-encode users could see another 2-3× - Wheels / PyPI release — install from source for now
License
BSD-3-Clause. Vendored components retain their original licenses (see 3rdparty/).
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