FFMPEG HDF5 Filter Enables High-Ratio Image Compression for Faithful Scientific Analysis

The FFMPEG HDF5 filter enables high-ratio compression of scientific datasets in HDF5 files using video codec technology. It supports a wide range of codecs (H.264, H.265/HEVC, AV1, etc.) with hardware acceleration options for NVIDIA GPUs and Intel QuickSync.

Features

• High Compression Ratios: Achieve 10-10,000× compression while preserving analysis fidelity
• Multiple Codec Support: H.264, H.265/HEVC, AV1, and more
• Hardware Acceleration: NVIDIA GPU and Intel QuickSync support
• Simple Python API: Easy-to-use interface for H5Py
• Automated Optimization: Film grain synthesis and artifact minimization
• Cross-Platform: Works on Linux, macOS, and Windows
• ImageJ/Fiji Plugin Support: Direct visualization and analysis in popular scientific imaging tools

Installation

Via pip (recommended)

pip install h5ffmpeg

Using pre-built binaries for ImageJ/Fiji

We recommend using our imageJ update sites.

Note: Due to the limitation of static built ffmpeg to comply with Java, some codecs are not supported. We strongly recommend using our python package. If working with large-scale dataset, SISF_CDN with neuroglancer is recommended.

From source (not recommended)

git clone https://github.com/Cai-Lab-at-University-of-Michigan/ffmpeg_HDF5_filter.git
cd ffmpeg_HDF5_filter
pip install -e .

This is not recommended since it requires compiling FFmpeg from source with HDF5 support, which is complex and error-prone. Our pip package includes pre-built, tested binaries.

Quick Start

import h5py
import numpy as np
import h5ffmpeg as hf

# Create sample data
data = np.random.rand(100, 512, 512).astype(np.uint8)

# Save with default settings (H.264)
with h5py.File("compressed.h5", "w") as f:
    f.create_dataset("data", data=data, **hf.x264())

# Save with H.265/HEVC compression
with h5py.File("compressed_hevc.h5", "w") as f:
    f.create_dataset("data", data=data, **hf.x265(crf=28))

# Save with AV1 compression (highest ratio)
with h5py.File("compressed_av1.h5", "w") as f:
    f.create_dataset("data", data=data, **hf.svtav1(crf=30))

# Use with NVIDIA GPU acceleration
with h5py.File("compressed_gpu.h5", "w") as f:
    f.create_dataset("data", data=data, **hf.h264_nvenc())

Advanced Usage

Custom Codec Configuration

import h5ffmpeg as hf

# Access the full ffmpeg API for complete control
compression_options = hf.ffmpeg(
    codec="libx264",        # Codec to use
    preset="medium",        # Encoding speed vs compression efficiency
    tune="film",            # Content-specific optimization
    crf=23,                 # Quality level (lower = higher quality)
    bit_mode=hf.BitMode.BIT_10,  # 8, 10, or 12-bit encoding
    film_grain=50,          # Film grain synthesis (0-50)
    gpu_id=0                # GPU ID (Default: 0)
)

Automated Hardware Acceleration

The library can detect and use available hardware acceleration:

import h5ffmpeg as hf

# This will automatically use NVIDIA GPU if available, 
# or fall back to CPU if not
compression_options = hf.ffmpeg(
    codec="h264_nvenc" if hf.has_nvidia_gpu() else "libx264",
    preset="p4" if hf.has_nvidia_gpu() else "medium",
    crf=23
)

Available Codecs

Codec	Implementation	Description	Typical Use Case
XVID	libxvid	MPEG-4 codec	Legacy support
H.264	libx264	General-purpose codec	Good balance of quality and speed
H.265/HEVC	libx265	Higher efficiency than H.264	Better compression for same quality
AV1	libsvtav1	Next-gen open codec	Highest compression ratio
AV1	librav1e	Rust AV1 encoder	Alternative AV1 implementation
H.264 NVENC	h264_nvenc	NVIDIA GPU-accelerated H.264	Fast encoding on NVIDIA GPUs
HEVC NVENC	hevc_nvenc	NVIDIA GPU-accelerated HEVC	High-quality, fast encoding on NVIDIA GPUs
AV1 NVENC	av1_nvenc	NVIDIA GPU-accelerated AV1	Next-gen encoding on newest NVIDIA GPUs
AV1 QSV	av1_qsv	Intel QuickSync AV1	Hardware acceleration on Intel GPUs

Compatibility

• Python: 3.11+
• Operating Systems: Linux-x86_64, macOS Apple Silicon, and Windows-AMD64
• hdf5: 1.14+
• h5py: 3.8+

License

MIT License

Citation

If you use this software in your research, please cite:

Duan, B., Walker, L.A., Xie, B., Lee, W.J., Lin, A., Yan, Y., and Cai, D. (2024).
Artifact-Minimized High-Ratio Image Compression with Preserved Analysis Fidelity.

Acknowledgments

This work was funded by the United States National Institutes of Health (NIH) grants RF1MH123402, RF1MH124611, and RF1MH133764.

Community and Support

• GitHub Issues: For bug reports and feature requests
• Contact: Feel free to reach out to us with questions

Related Projects

Feel free to check out other tools from the Cai Lab:)

• nGauge: Python library for neuron morphology analysis
• nTracer2: Browser-based tool for neuron tracing
• pySISF: Python wrapper for SISF format
• SISF_CDN:Scalable Image Storage Format CDN