Image MIME Reference
FITS
Flexible Image Transport System
image/fits
VS
JPEG AI Image
image/jaii
JPEG-AI
A complete technical comparison of Flexible Image Transport System and JPEG AI Image — covering compression, feature support, browser compatibility, magic bytes, and when to choose each format.
Equal browser support
Feature Support
| Feature | FITS | JPEG-AI |
|---|---|---|
| Transparency (Alpha) | ✕ No | Yes |
| Animation Support | ✕ No | Yes |
| Progressive Loading | ✕ No | Yes |
| HDR Support | Yes | Yes |
| EXIF Metadata | ✕ No | Yes |
| ICC Color Profile | ✕ No | Yes |
Flexible Image Transport System
image/fits
Extension
.fits.fit.fts
Container
FITS
Compression
Uncompressed / Tile Compressed (Rice, GZIP)
Algorithm
UncompressedRiceGZIPHcompress
Color Depth
8-bit, 16-bit, 32-bit, 64-bit (Float/Double)
Developed by
IAU FITS Working Group / NASA
Released
1981
Magic Bytes
53 49 4D 50 4C 45 20 20 3D 20
JPEG AI Image
image/jaii
Extension
.jaii
Container
ISOBMFF / HEIF
Compression
Lossy (Variational Autoencoder / Neural Network)
Algorithm
Deep Neural Network (Variational Autoencoder with Hyperpriors)
Color Depth
8-bit, 10-bit, 12-bit, 16-bit, Wide-gamut & HDR
Developed by
Joint Photographic Experts Group (ISO/IEC JTC 1 / ITU-T)
Released
2025
Magic Bytes
.. .. .. .. 66 74 79 70 6A 61 69 69 (Size + ftypjaii)
Browser Support Comparison
| Browser | FITS | JPEG-AI |
|---|---|---|
Chrome
|
✕ | ✕ |
Firefox
|
✕ | ✕ |
Safari
|
✕ | ✕ |
Edge
|
✕ | ✕ |
IE (Legacy)
|
✕ | ✕ |
FITS
FITS Strengths
- The undisputed global standard for archiving and sharing astronomical data
- Self-documenting: Headers are written in plain ASCII, ensuring data is readable decades later regardless of software
- Supports incredibly high dynamic range, N-dimensional arrays, and complex binary tables
Limitations
- No support in standard consumer software or web browsers
- Uncompressed raw FITS files from modern telescopes can be excessively large
- Does not use standard metadata models like EXIF or XMP, requiring specialized parsers
JPEG-AI
JPEG-AI Strengths
- Provides state-of-the-art compression efficiency, dramatically outperforming traditional block-transform codecs like JPEG or HEIC [2.2.2]
- Allows machine vision models to process the raw 'latent tensors' in the compressed stream natively, drastically reducing inference latency by bypassing image reconstruction
- Supports 'multi-branch decoding', enabling a single codestream to be decoded at varying complexity levels depending on the target hardware's power (e.g., mobile NPU vs. cloud GPU)
Limitations
- Lacks native decoding support in current web browsers and legacy operating systems [3.2.1]
- Decoding relies on neural network inference which can be heavily computationally demanding on hardware lacking dedicated AI accelerators (NPUs/GPUs)
- Introduces new vectors for security and forensic analysis, as end-to-end learned structures react differently to adversarial attacks compared to traditional formats
When to choose which format
FITS
Use FITS when…
- Astronomy & Astrophysics
- Space Telescope Data (Hubble, JWST)
- Scientific Data Archiving
- Spectroscopy
JPEG-AI
Use JPEG-AI when…
- Machine Vision and AI Workflows
- Cloud Storage Image Optimization
- High-Efficiency Mobile Image Capture
- Visual Surveillance