Convert FITS to QOI

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FITS vs QOI Format Comparison

Aspect	FITS (Source Format)	QOI (Target Format)
Format Overview	FITS Flexible Image Transport System Scientific image format developed by NASA and the International Astronomical Union FITS Working Group (IAUFWG), first defined in 1981. Supports 8/16/32/64-bit integer and 32/64-bit floating-point pixel data with multi-extension architecture for storing multiple images and tables per file. Includes WCS (World Coordinate System) metadata for celestial coordinate mapping. The standard data format for astronomical observatories worldwide. Lossless Standard	QOI Quite OK Image Format Minimalist lossless image format created by Dominic Szablewski in 2021. Achieves PNG-comparable compression with 20-50x faster encoding and 3-4x faster decoding. Uses a simple 300-line reference implementation with only 4 operations: index, diff, luma, run. Modern Format Lossless
Technical Specifications	Data Types: 8/16/32/64-bit integer, 32/64-bit float Structure: Multi-extension (images, tables, headers) Metadata: WCS celestial coordinates, extensive headers Byte Order: Big-endian (FITS standard) Extensions: .fits, .fit, .fts	Color Depth: 24-bit RGB or 32-bit RGBA Compression: Lossless (index, diff, luma, run) Speed: 20-50x faster encode than PNG Header: 14-byte fixed header Extensions: .qoi
Image Features	Data Types: Integer (8-64 bit) and floating-point (32-64 bit) Multi-Extension: Multiple images and binary tables per file WCS Metadata: World Coordinate System for celestial mapping Header Keywords: Extensive ASCII keyword-value metadata Dynamic Range: Full floating-point for scientific flux data Coordinate Systems: Equatorial, galactic, ecliptic reference frames	Ultra-fast lossless compression Minimal 300-line implementation 24-bit RGB and 32-bit RGBA Streaming encode/decode Fixed 14-byte header No configuration parameters
Processing & Tools	FITS data handling with astropy and Python: from astropy.io import fits import numpy as np # Open FITS file with full header access hdul = fits.open('observation.fits') header = hdul[0].header # WCS, telescope info data = hdul[0].data # Pixel array # Access multi-extension data for ext in hdul: print(ext.name, ext.data.shape if ext.data is not None else 'No data')	QOI lossless image from FITS data: from astropy.io import fits from PIL import Image import numpy as np hdul = fits.open('globular.fits') data = np.clip(hdul[0].data, 0, 255).astype('uint8') img = Image.fromarray(data) img.save('globular.qoi')
Advantages	Full floating-point dynamic range for scientific data Multi-extension architecture for complex datasets WCS metadata preserves celestial coordinate information Extensive header keywords for observation metadata Universal standard across all astronomical observatories Supported by every major astronomical software package	20-50x faster encoding than PNG 3-4x faster decoding than PNG Simple, auditable implementation Lossless quality preservation Pillow 12+ native support Streaming encode/decode capability
Disadvantages	Not viewable in standard image viewers or browsers Requires specialized astronomical software Large file sizes for high-resolution observations Big-endian byte order can cause processing overhead Complex multi-extension structure	Slightly larger files than optimized PNG Limited to 8-bit per channel Very new format (2021) Limited software ecosystem No animation support No metadata or ICC profiles
Common Uses	Space telescope observations (Hubble, JWST, Chandra) Ground observatory data (VLT, Keck, Gemini) Sky survey archives (SDSS, 2MASS, Gaia) Solar observation data (SDO, SOHO) Radio astronomy imaging (ALMA, VLA)	Real-time image capture and storage Game engine screenshots Fast lossless intermediate format Embedded systems with limited CPU High-throughput image pipelines
Best For	Scientific astronomical observations with precise flux data Multi-band imaging campaigns requiring coordinated datasets Archival storage with full observation metadata Pipeline processing requiring WCS coordinate transforms	Fast lossless storage of processed astronomical frames Real-time telescope image capture preview High-throughput observatory data pipelines Quick lossless interchange in astronomical software
Version History	Introduced: 1981 (NASA/IAU FITS Working Group) Current: FITS Standard 4.0 (2018) Status: Active, universal astronomical standard Evolution: FITS 1.0 (1981) → 2.0 (1988) → 3.0 (2008) → 4.0 (2018)	Introduced: 2021 (Dominic Szablewski) Specification: QOI 1.0 (stable) Status: Active, growing adoption Evolution: QOI 1.0 (2021) → stable specification
Software Support	Astronomy: ds9, IRAF, PixInsight, Aladin, TOPCAT Libraries: astropy (Python), cfitsio (C), FITSIO (IDL) Space Agencies: NASA HEASARC, ESA archives, MAST Other: ImageMagick, GIMP (via plugin), Pillow (limited)	Libraries: Pillow 12+, qoi (Python), libqoi (C) Viewers: IrfanView, XnView, qoiview Editors: GIMP (via plugin), limited editor support Other: 100+ implementations in various languages

Why Convert FITS to QOI?

Converting FITS to QOI creates lossless astronomical images with the fastest possible encoding speed. QOI encodes 20-50x faster than PNG with comparable compression, making it ideal for real-time telescope preview displays and high-throughput survey data pipelines.

Modern sky surveys process thousands of FITS frames per night, generating quick-look preview images for quality monitoring. QOI's ultra-fast encoding means preview images are ready almost instantly, without the encoding bottleneck that PNG creates in high-throughput workflows.

Telescope control systems displaying real-time CCD readouts benefit from QOI's minimal encoding latency. The format's streaming encode/decode design means pixels can be processed as they arrive from the detector, enabling the lowest-latency lossless preview possible.

The conversion maps FITS data to 8-bit RGB and applies QOI's four-operation compression (index, diff, luma, run). The resulting files are slightly larger than optimized PNG but encode in a fraction of the time, which is the key advantage for latency-sensitive applications.

Key Benefits of Converting FITS to QOI:

Ultra-Fast Encoding: 20-50x faster than PNG, enabling real-time astronomical image preview
Lossless Guarantee: Every pixel preserved exactly for scientific data integrity
Fast Decoding: 3-4x faster than PNG decode for responsive telescope control displays
Streaming Design: Encode/decode pixels as they arrive from the CCD detector
Minimal CPU: Simple algorithm suitable for embedded observatory computing systems
Pipeline Speed: High-throughput survey pipelines benefit from near-zero encoding latency
Simple Implementation: 300-line reference codec means easy integration into custom astronomy software

Practical Examples

Example 1: Real-Time Telescope Preview

Scenario: A telescope control system generates real-time preview images of incoming CCD frames and needs the fastest possible lossless encoding for display.

Input FITS file (live_preview.fits):

FITS astronomical data:
  Resolution: 2048×2048 CCD preview
  Data: Real-time CCD readout
  Instrument: Observatory CCD imager
  Content: Live observing field

Output QOI file (live_preview.qoi):

Converted QOI output:
  20-50x faster than PNG encode
  Lossless quality guaranteed
  Minimal CPU overhead
  Sub-millisecond encoding

Example 2: High-Throughput Survey Pipeline

Scenario: A sky survey pipeline processing thousands of frames per night needs a fast lossless format for quick-look thumbnails without slowing the data flow.

Input FITS file (survey_frame.fits):

FITS astronomical data:
  Resolution: 1024×1024 thumbnail
  Data: Automated survey data
  Instrument: Wide-field survey camera
  Content: Survey field quick-look

Output QOI file (survey_frame.qoi):

Converted QOI output:
  Ultra-fast encode/decode
  Streaming capable
  Pipeline-friendly speed
  Lossless quality preserved

Example 3: Observatory Monitoring Dashboard

Scenario: An observatory monitoring system captures periodic snapshots of all-sky camera and telescope feeds, needing fast lossless storage for later review.

Input FITS file (monitoring_snap.fits):

FITS astronomical data:
  Resolution: 1280×960 monitoring frame
  Data: All-sky camera capture
  Instrument: SBIG all-sky camera
  Content: Night sky conditions

Output QOI file (monitoring_snap.qoi):

Converted QOI output:
  Rapid capture storage
  3-4x faster decode than PNG
  Simple implementation
  Reliable lossless storage

Frequently Asked Questions (FAQ)

Q: What is FITS format?

A: FITS (Flexible Image Transport System) is the standard data format for astronomical observations, developed by NASA and the IAU since 1981. It stores scientific data with floating-point precision and World Coordinate System metadata.

Q: What is QOI format?

A: QOI (Quite OK Image) is a minimalist lossless format created by Dominic Szablewski in 2021. It achieves PNG-comparable compression at 20-50x faster encoding and 3-4x faster decoding using only four simple operations: index, diff, luma, and run.

Q: Why convert FITS to QOI?

A: QOI's ultra-fast encoding makes it ideal for real-time astronomical applications: telescope preview displays, high-throughput survey pipelines, and monitoring systems where encoding speed is critical and lossless quality is required.

Q: How does QOI compare to PNG for astronomical images?

A: QOI encodes 20-50x faster and decodes 3-4x faster than PNG, with files typically 10-20% larger. For astronomical images with many unique pixel values (noise, star fields), QOI may produce slightly larger files than optimized PNG.

Q: Is QOI suitable for archival storage?

A: QOI is lossless and suitable for data preservation. However, its 2021 introduction means long-term software support is less certain than PNG (1996). For archival purposes, PNG or TIFF may be safer choices with their longer track records.

Q: Does QOI support 16-bit or floating-point data?

A: No, QOI is limited to 8-bit per channel (24-bit RGB or 32-bit RGBA). FITS data must be scaled to 8-bit range during conversion. For higher bit-depth preservation, use PNG (16-bit) or TIFF (32-bit float).

Q: What software supports QOI?

A: Pillow 12+ has native QOI read/write support. Over 100 implementations exist in various programming languages. IrfanView and XnView support QOI viewing. Web browser support is currently not available.

Q: When should I choose QOI over PNG?

A: Choose QOI when encoding/decoding speed is more important than file size (real-time display, high-throughput pipelines). Choose PNG when file size matters or when broad software compatibility is essential.