Image MIME Reference
JPEG-AI
JPEG AI Image
image/jaii
VS
Microsoft Document Imaging
image/vnd.ms-modi
MODI
A complete technical comparison of JPEG AI Image and Microsoft Document Imaging — covering compression, feature support, browser compatibility, magic bytes, and when to choose each format.
Equal browser support
Feature Support
| Feature | JPEG-AI | MODI |
|---|---|---|
| Transparency (Alpha) | Yes | ✕ No |
| Animation Support | Yes | ✕ No |
| Progressive Loading | Yes | ✕ No |
| HDR Support | Yes | ✕ No |
| EXIF Metadata | Yes | ✕ No |
| ICC Color Profile | Yes | ✕ No |
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)
Microsoft Document Imaging
image/vnd.ms-modi
Extension
.mdi
Container
TIFF Variant
Compression
LZW / Proprietary MS Compression
Algorithm
LZW (Modified)
Color Depth
1-bit (Monochrome), 8-bit (Grayscale), 24-bit (RGB)
Developed by
Microsoft Corporation
Released
2001
Magic Bytes
49 49 2A 00
Browser Support Comparison
| Browser | JPEG-AI | MODI |
|---|---|---|
Chrome
|
✕ | ✕ |
Firefox
|
✕ | ✕ |
Safari
|
✕ | ✕ |
Edge
|
✕ | ✕ |
IE (Legacy)
|
✕ | ✕ |
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
MODI
MODI Strengths
- Historically provided an excellent all-in-one scanned document format with built-in OCR and annotations before PDF editing became universally accessible
- Tightly integrated with Microsoft Office and SharePoint workflows in the early 2000s
Limitations
- Completely proprietary and obsolete; Microsoft abandoned it in favor of standard TIFF and PDF
- Causes widespread identification conflicts since its file signature is identical to a standard TIFF
- Extremely difficult to open on modern operating systems without relying on third-party conversion utilities
When to choose which format
JPEG-AI
Use JPEG-AI when…
- Machine Vision and AI Workflows
- Cloud Storage Image Optimization
- High-Efficiency Mobile Image Capture
- Visual Surveillance
MODI
Use MODI when…
- Legacy Scanned Document Archival
- Microsoft Office XP/2003/2007 Workflows
- Embedded OCR Text Storage