Free webp to jpg
EN
Image MIME Reference
DGN

MicroStation Design File

image/x-vnd.dgn
VS

JPEG AI Image Sequence

image/jais
JPEG-AI-Sequence

A complete technical comparison of MicroStation Design File 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 DGN JPEG-AI-Sequence
Transparency (Alpha) Yes Yes
Animation Support ✕ No Yes
Progressive Loading ✕ No Yes
HDR Support ✕ No Yes
EXIF Metadata ✕ No Yes
ICC Color Profile ✕ No Yes
MicroStation Design File
image/x-vnd.dgn
Extension
.dgn
Container Microsoft OLE Compound File (V8) / IGDS (V7)
Compression Uncompressed / OLE Structured Storage
Algorithm
None
Color Depth Indexed Color (V7), 24-bit True Color (V8)
Developed by Intergraph (Originally) / Bentley Systems (Currently)
Released 1980
Magic Bytes
D0 CF 11 E0 A1 B1 1A E1 (For V8 DGN)
Full DGN reference →
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 →

Browser Support Comparison

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

DGN Strengths

  • Exceptionally robust at handling massive, coordinate-heavy infrastructure models that would frequently crash older versions of competing CAD software
  • Deeply entrenched as the absolute standard format for many global departments of transportation and civil engineering firms
  • V8 architecture supports boundless design plane coordinates, eliminating the physical scaling limitations of V7
Limitations
  • Deeply proprietary format requiring complex third-party libraries (like the ODA SDK) to parse or write programmatically outside of the Bentley ecosystem
  • V8's reliance on the archaic Microsoft OLE Compound File container makes file introspection cumbersome
  • Cannot be rendered natively in web browsers without converting to WebGL/Canvas formats or SVG
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

DGN
Use DGN when…
  • Civil Engineering and Plant Design
  • Large-scale Architecture and Construction (AEC)
  • 2D/3D Infrastructure Modeling
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

More comparisons

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