Image MIME Reference
CMU-Raster
CMU Window Manager Raster
image/x-cmu-raster
VS
JPEG AI Image Sequence
image/jais
JPEG-AI-Sequence
A complete technical comparison of CMU Window Manager Raster and JPEG AI Image Sequence — covering compression, feature support, browser compatibility, magic bytes, and when to choose each format.
Equal browser support
Feature Support
| Feature | CMU-Raster | JPEG-AI-Sequence |
|---|---|---|
| Transparency (Alpha) | ✕ No | Yes |
| Animation Support | ✕ No | Yes |
| Progressive Loading | ✕ No | Yes |
| HDR Support | ✕ No | Yes |
| EXIF Metadata | ✕ No | Yes |
| ICC Color Profile | ✕ No | Yes |
CMU Window Manager Raster
image/x-cmu-raster
Extension
.ras
Container
CMU Window Manager Bitmap
Compression
Uncompressed
Algorithm
None
Color Depth
1-bit (Monochrome), 8-bit (Indexed/Grayscale)
Developed by
Carnegie Mellon University (CMU)
Released
1980s
Magic Bytes
F1 00 40 BB
JPEG AI Image Sequence
image/jais
Extension
.jais
Container
ISOBMFF / HEIF Image Sequence
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 73 (Size + ftypjais)
Browser Support Comparison
| Browser | CMU-Raster | JPEG-AI-Sequence |
|---|---|---|
Chrome
|
✕ | ✕ |
Firefox
|
✕ | ✕ |
Safari
|
✕ | ✕ |
Edge
|
✕ | ✕ |
IE (Legacy)
|
✕ | ✕ |
CMU-Raster
CMU-Raster Strengths
- Incredibly simple, uncompressed binary structure that was easy for early, memory-constrained UNIX workstations to parse and render
Limitations
- Completely obsolete and unsupported outside of niche historical command-line tools like Netpbm
- The shared '.ras' file extension causes frequent file misidentification and system conflicts
- Lacks all modern imaging features, including metadata, compression, and alpha channel transparency
JPEG-AI-Sequence
JPEG-AI-Sequence Strengths
- Extends the state-of-the-art compression efficiency of JPEG AI to multi-frame image sequences and short animations
- Allows machine vision algorithms to process temporal sequences (like surveillance bursts) directly in the compressed latent domain, saving immense computational power
- Utilizes the robust, industry-standard ISOBMFF container for metadata, timing, and multi-track encapsulation
Limitations
- Lacks native decoding support in current web browsers, operating systems, and video players
- Decoding neural-network-compressed sequences requires significant compute overhead on hardware lacking dedicated AI accelerators (NPUs/GPUs)
- Adoption is hindered by competition with established sequence formats like AVIF, HEVC (HEICS), and modern video codecs
When to choose which format
CMU-Raster
Use CMU-Raster when…
- Historical UNIX / Andrew Project Archival
- Legacy Academic Computing Research
JPEG-AI-Sequence
Use JPEG-AI-Sequence when…
- Machine Vision and AI Video/Burst Workflows
- Cloud Storage Animation Optimization
- Focal Stacks and Medical Volumetric Slices
- Visual Surveillance Sequences