Free webp to jpg
EN
Image MIME Reference
DICOM-RLE

DICOM Run-Length Encoding

image/dicom-rle
VS

JPEG AI Image Sequence

image/jais
JPEG-AI-Sequence

A complete technical comparison of DICOM Run-Length Encoding 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 DICOM-RLE JPEG-AI-Sequence
Transparency (Alpha) ✕ No Yes
Animation Support Yes Yes
Progressive Loading ✕ No Yes
HDR Support Yes Yes
EXIF Metadata ✕ No Yes
ICC Color Profile Yes Yes
DICOM Run-Length Encoding
image/dicom-rle
Extension
.dcm.dicom
Container DICOM (PS3.10)
Compression Lossless (RLE)
Algorithm
Run-Length Encoding (RLE)
Color Depth 8-bit, 16-bit, 24-bit
Developed by National Electrical Manufacturers Association (NEMA) / ACR
Released 1993
Magic Bytes
44 49 43 4D (at byte offset 128)
Full DICOM-RLE 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 DICOM-RLE JPEG-AI-Sequence
Chrome Chrome
✕ ✕
Firefox Firefox
✕ ✕
Safari Safari
✕ ✕
Edge Edge
✕ ✕
IE IE (Legacy)
✕ ✕
DICOM-RLE

DICOM-RLE Strengths

  • Guarantees lossless preservation of critical medical data necessary for diagnostics
  • Extremely robust metadata structure containing patient information, modality, and spatial geometry
  • Faster to encode and decode than complex compression schemes like JPEG 2000
Limitations
  • RLE compression offers very low compression ratios compared to modern codecs like JPEG-LS or JPEG 2000
  • Zero support in web browsers or standard consumer image viewers
  • Strict data privacy regulations (HIPAA/GDPR) make handling DICOM files complex due to embedded PHI
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

DICOM-RLE
Use DICOM-RLE when…
  • Medical Imaging
  • Ultrasound
  • X-Rays / CT / MRI
  • PACS Archives
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

DICOM-RLE vs ACES → JPEG-AI-Sequence vs ACES → DICOM-RLE vs APNG → JPEG-AI-Sequence vs APNG → DICOM-RLE vs AVCI → JPEG-AI-Sequence vs AVCI → DICOM-RLE vs AVCS → JPEG-AI-Sequence vs AVCS → DICOM-RLE vs AVIF → JPEG-AI-Sequence vs AVIF → All comparisons →
Share this comparison