opencodecs 0.1.13

Streaming, network-aware image codecs for scientific imaging (prototype: JPEG XL)

opencodecs

Native, parallel, cloud-aware codecs for scientific imaging. One unified Codec / Reader / Writer API across compression streams, single images, multi-frame stacks, and chunked containers — with HTTP range-fetch and per-chunk parallelism wired in at the bottom of the stack, not bolted on.

Built for fast modern storage (NVMe, 10 G NAS, S3) 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.

pip install opencodecs

import opencodecs as oc

# 1. Look at any scientific image file
arr = oc.read("scan.czi")              # auto-detect by extension
arr = oc.read("photo.jxl")
arr = oc.read(blob)                    # auto-detect by magic bytes

# 2. Write with the right codec for the data
oc.write("out.jxl", arr, lossless=True)
oc.write("out.zst", b"...payload...", level=10)

# 3. Stream 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

# 4. Fetch tiles of a remote pyramidal TIFF over HTTPS by range request
with oc.open_pyramid("https://example.com/slide.svs") as p:
    region = p.read_region(level=2, y=(1024, 2048), x=(1024, 2048))
    # → 2-3 HTTP Range requests, not a full slide download

# Discovery
oc.list_codecs()                       # capability table
oc.has_codec("avif")

Why opencodecs

Need	What you get
Decode regions of cloud-hosted TIFF/Zarr/HDF5 without downloading the whole file	Native HTTPDataSource with range-coalescing + adaptive read-ahead, wired into the TIFF/NDTiff/HDF5/Zarr/FITS pyramid readers
Per-chunk parallel decode of CZI/OME-TIFF/NDTiff stacks	Built-in ThreadPoolExecutor orchestration with nogil-released codec calls; 3–10× over single-threaded reference readers on large stacks
Modern codec coverage (JPEG XL, AVIF, HEIF, JPEG-LS, Brunsli, Ultra HDR, OME-Zarr v3 sharded)	All shipped, all with native bindings — no pip install ten-other-packages
Tier-1 scientific compressors (LERC, ZFP, SZ3, SPERR, pcodec, bitshuffle, blosc2, libaec)	All shipped, source-built with -O3 + LTO + hidden-visibility for Pareto wins over distro builds
Lossless drop-in replacement for imagecodecs	tifffile_patch opt-in shim reroutes tifffile's codec dispatch through opencodecs without changing your tifffile code

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 beats zstd by 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 beats zfp at 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

Streaming-reader examples

1. Fetch a region of a remote Aperio whole-slide TIFF

import opencodecs as oc

# Pyramidal SVS (Aperio) hosted on S3 / any HTTPS endpoint with Range support.
with oc.open_pyramid("https://example.com/slide.svs") as p:
    print(p.levels)               # [(80000, 60000, 3), (40000, 30000, 3), ...]
    region = p.read_region(level=2, y=(1024, 3072), x=(2048, 4096))
    # Total HTTP traffic: ~6 Range requests covering only the tiles
    # that intersect this 2048×2048 bbox — typically 200 KB–2 MB,
    # not the 4 GB whole slide.

The pyramid reader auto-detects the best level for the requested region, fetches only the intersecting TIFF tiles via HTTP Range, and assembles the output in-memory. Works the same on local files, NFS, SMB, S3, or any range-capable HTTP server.

2. Convert a multi-level pyramid to OME-Zarr v3 sharded

import opencodecs as oc

with oc.open_pyramid("input.ome.tiff") as p:
    levels = [p.read_region(level=i) for i in range(len(p.levels))]

oc.write_omezarr_pyramid(
    "output.zarr",
    levels,
    chunks=(512, 512),
    shards=(2048, 2048),         # 16 chunks per shard, one file each
    compressor="zstd",
    zarr_format=3,
)
# 1 file per shard on disk instead of 1 file per chunk; per-chunk
# random access still works via Range fetches into the shard.

For data going to S3, sharded Zarr v3 cuts your PUT and LIST costs by 1–2 orders of magnitude vs unsharded chunks while preserving per-chunk random-access via HTTP Range — the reader above understands the shard index automatically.

3. Fast JPEG XL thumbnails (native progressive decode)

import opencodecs.jxl as jxl

# downsample=8 uses libjxl's native progressive decoder — stops at
# the DC pass without reconstructing full-resolution pixels.
thumb = jxl.read("scan.jxl", downsample=8, subsample="center")
# 4Kx4K input → 512x512 ndarray in ~28 ms on macOS arm64
# (vs ~40 ms for a full decode), positionally centroid-correct
# so SVG / GL renderers don't get a ½-block shift.

# For a partial JXL bitstream usable as a tiny browser-direct
# thumbnail (works in Safari + modern Chrome):
prefix = jxl.thumbnail_bytes("scan.jxl")
# → ~85 KB out of a 3.5 MB source for a 4Kx4K image

Install

pip install opencodecs

Wheels are published for CPython 3.10–3.13 on macOS (arm64), Linux (x86_64 + aarch64), and Windows (amd64). Each wheel bundles libjxl, libavif, libheif, libwebp, libdeflate, c-blosc2, and friends — no system dependencies needed.

For a source install, system development headers, or to build a tuned local libjxl, see INSTALL.md. Wheel publishing runs through docs/publishing.md.

# Source install — auto-detects system libs, source-builds libjxl
git clone https://github.com/kevinjohncutler/opencodecs.git
cd opencodecs
pip install -e .

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 auto-built from source via bench/build_libjxl.sh and cached under ~/Library/Caches/opencodecs/ (macOS) / ~/.cache/opencodecs/ (Linux). See INSTALL.md for the rationale (Homebrew/apt builds are 0.5-0.7× slower than a tuned -O3 + LTO build).

Status

• v0.1.1 on PyPI (May 2026). Core API stable; 1066 tests passing on Mac M1 Ultra + Linux x86_64/aarch64 + Windows VM
• Native readers + writers for the common scientific containers (TIFF, BigTIFF, OME-TIFF, CZI, NDTiff, HDF5, JXL, FITS, OME-Zarr v2 + v3 sharded)
• Cross-platform bench coverage: Mac arm64 (canonical), Windows 11 LTSC (libvirt VM), Linux x86_64 (Threadripper-class)
• Compression backend auto-detect (libdeflate → zlib-ng-compat → stdlib)
• Cloud I/O primitives (HTTPDataSource with covering-cache + adaptive read-ahead) wired into TIFF / HDF5 / DICOMweb / CZI / FITS / Zarr v3 readers
• tifffile_patch opt-in shim reroutes tifffile's codec dispatch through opencodecs for users who want only a partial swap

Deferred work (see docs/TODO_DEFERRED.md):

• Windows wheels currently miss _sz3, _pcodec, _sperr, _brunsli — toolchain mismatch (conda's bash picks GCC over MSVC for CMake); v0.1.2 will restore them. macOS + Linux wheels have the full set.
• CCITT Fax3/Fax4 encode — legacy fax; zero scientific users
• JPEG-XR — abandoned format outside niche DICOM
• libspng filter_sum SIMD — off the bench-tracked workload (h2h_png_4mp_rgb is at 1.14× already); filter-bound PNG-encode users could see another 2-3×

License

BSD-3-Clause. Vendored components retain their original licenses (see 3rdparty/).