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Image MIME Reference
JPEG-AI-Sequence

JPEG AI Image Sequence

image/jais
VS

Silicon Graphics Image

image/x-rgb
SGI-RGB

A complete technical comparison of JPEG AI Image Sequence and Silicon Graphics Image — covering compression, feature support, browser compatibility, magic bytes, and when to choose each format.

Equal browser support

Feature Support

Feature JPEG-AI-Sequence SGI-RGB
Transparency (Alpha) Yes Yes
Animation Support Yes ✕ No
Progressive Loading Yes ✕ No
HDR Support Yes ✕ No
EXIF Metadata Yes ✕ No
ICC Color Profile Yes ✕ No
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)
Full JPEG-AI-Sequence reference →
Silicon Graphics Image
image/x-rgb
Extension
.rgb.sgi.rgba.bw.int.inta
Container SGI Image Header (512 bytes)
Compression Uncompressed / Run-Length Encoding (RLE)
Algorithm
NoneScanline RLE
Color Depth 8-bit (Per Channel), 16-bit (Per Channel)
Developed by Silicon Graphics, Inc. (Paul Haeberli)
Released 1988
Magic Bytes
01 DA
Full SGI-RGB reference →

Browser Support Comparison

Browser JPEG-AI-Sequence SGI-RGB
Chrome Chrome
✕ ✕
Firefox Firefox
✕ ✕
Safari Safari
✕ ✕
Edge Edge
✕ ✕
IE IE (Legacy)
✕ ✕
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
SGI-RGB

SGI-RGB Strengths

  • Supports 16-bit precision per channel and an embedded alpha channel, making it ideal for the demanding compositing workflows of 1990s cinema VFX
  • The scanline-based RLE compression was exceptionally fast to decode on classic MIPS-based SGI workstations
  • Features a straightforward 512-byte header, making it relatively easy to parse and write custom decoders in C or Python
Limitations
  • Completely obsolete and unsupported in modern web environments
  • Replaced entirely by TIFF, OpenEXR, and PNG in modern visual effects and 3D rendering pipelines
  • Lacks support for modern color management via ICC profiles or advanced EXIF metadata

When to choose which format

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
SGI-RGB
Use SGI-RGB when…
  • Legacy 3D Animation and VFX Textures
  • Silicon Graphics IRIX Workstation Archival
  • 1990s Medical Imaging

More comparisons

JPEG-AI-Sequence vs ACES → SGI-RGB vs ACES → JPEG-AI-Sequence vs APNG → SGI-RGB vs APNG → JPEG-AI-Sequence vs AVCI → SGI-RGB vs AVCI → JPEG-AI-Sequence vs AVCS → SGI-RGB vs AVCS → JPEG-AI-Sequence vs AVIF → SGI-RGB vs AVIF → All comparisons →
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