RIPT is the core of the Bitruvius Imagery & 3D Suite — adopt a new format for the largest structural wins.
Explore the SuiteEvery tile. Its own predictor.
A new raster codec that picks the best predictor for each tile, SIMD-encodes the result, and delivers extreme compression in lossy modes — elevation compresses up to 200× at 1m vertical error.
Performance
The numbers
that matter.
All medians come from the canonical Auto+Zstd configuration across the matched corpus. Peaks are footnoted, not headlined.
median across the corpus
prediction-friendly i32 with sparse outliers
median on multispectral · 256×256 tiles
single-threaded, AVX2
median compression at 1m vertical error
peak 0× on bare-earth DEM
median on Pacific seafloor (quantized basemap)
Mt Everest terrain 1,520× · a class apart
Lossless compression varies by domain. Where data structure rewards prediction, RIPT wins decisively (Classification, Multispectral). Where data is noise-dominated (SAR magnitude), RIPT bypasses prediction entirely and routes bytes to the entropy coder — no penalty for being asked to compress unpredictable data.
Prove It
Race it yourself. Right here.
RIPT decodes scientific rasters head-to-head against TurboLERC and the Esri LERC reference — live in your browser, no install. Pick a raster type and hit START.
Cross-Domain
Absolute compression. Per domain.
The compression ratio RIPT actually delivers, by domain. Classification rasters and multispectral satellite imagery push past 6× lossless. Noisy SAR data is the floor — RIPT detects it and bypasses prediction.
Solid bar = median across the matched corpus.Faded bar = 90th percentile (clipped to chart bounds when extreme outliers stretch beyond).Peak column shows max across samples.
Solid bar = median across the matched corpus.Peak column shows max across samples.
Quantized basemap terrain. Smoothed, meter-quantized basemap DEMs (Mt Everest, Pacific seafloor) are far lower entropy than raw LiDAR, so RIPT collapses them orders of magnitude further — shown separately, on their own scale.
Solid bar = median across the matched corpus.Peak column shows max across samples.
Solid bar = median across the matched corpus.Peak column shows max across samples.
Lossy Mode
Pick a tolerance.
Pick a payoff.
Every value decodes within the tolerance you specify. The slider doubles as a compression dial — at 1m vertical error on elevation, RIPT delivers 87× median, 200×+ peak.
LiDAR Elevation (DEM, F32). Solid line: median across 3672 samples. Dashed: peak.
What if every region
chose its own strategy?
Consider a satellite image of a coastline. The ocean is smooth, so a simple predictor works best. The beach is a gradient that a first-order predictor captures naturally. The city has sharp edges where a multi-neighbor predictor excels. The harbor has SAR speckle noise where prediction actually hurts, so the codec should bypass it entirely.
Most codecs pick one strategy and apply it everywhere.
RIPT picks per region.
Same strategy everywhere.
Each region gets the predictor that fits.
Empirical
Different data,
different predictor.
This isn't a marketing claim. It's the empirical mix of predictors RIPT's auto-selector picked across thousands of test tiles. Classification data overwhelmingly wants Left. Elevation wants Quadratic and Planar. SAR Video is monolithic — Up wins everywhere.
Each row sums to 100% of samples in that domain. The mix is the data's, not the choice of any single predictor.
How It Works
Six steps.
One tile at a time.
Image broken into cache-friendly tiles. Sizes from 4×4 up through 32×32, tuned to the data.
A library of predictors compete: each predicts pixel values and measures residual errors. 13 ship today.
Winner = smallest encoded byte cost. Different tile, different winner.
Residuals undergo zigzag encoding → bit-packing at minimum bit-width.
Optional ByteShuffle / BitShuffle pre-compression transforms.
Entropy coder (Zstd / LZ4 / Deflate) squeezes remaining redundancy.
For high-entropy data like SAR radar, RIPT bypasses prediction entirely. Raw mode routes bytes straight to the entropy coder.
Novel IP
Four algorithms with
zero prior art.
ARP
Anchor-Residual Predictor
Recursive pairwise decomposition that concentrates energy into progressively smaller sets. O(N) complexity, vector-friendly.
NearFlat
Sparse Outlier Encoding
63 identical pixels and 1 outlier? Encodes the tile in 8 bytes instead of 64+. Used heavily on classification rasters and SAR magnitude.
Quadratic
Second-Order Polynomial
Captures surface curvature in rolling hills, river valleys, and thermal gradients that first-order predictors model only as flat planes. Wins ~47% of elevation tiles.
Sqrt
Variance-Stabilizing Transform
Converts multiplicative SAR speckle noise to additive noise. Makes the unpredictable predictable. Exactly invertible with zero information loss.
Performance & Portability
SIMD-first.
Bit-exact everywhere.
Every predictor is defined once at the algorithmic level and mapped to the widest SIMD available at runtime. A 256-bit AVX2 register processes 8 pixels in parallel. SVE2 hardware gets scalable-vector dispatch the moment its benchmarks land.
Platform matrix
All backends produce bit-exact identical output. Auto-detection at runtime; portable scalar fallback for any CPU.
Applications
Every industry that
stores grids of numbers.
Satellite Operators
Constellations capturing 30+ TB/day. Petabyte-scale archives. Tighter compression compounds across years of storage.
Aerial & Drone
$1.3B market. Unlocks 16-bit delivery from sensors capturing at 12-14 bit.
Medical Imaging
$4.2B PACS market. Tile-based decode replaces single-threaded gzip; faster pull on every viewer.
AI/ML Foundation Models
Native uint4/int4/bf16 support. NCHW/NHWC layout helpers for PyTorch & TensorFlow.
Government & Defense
NASA: 245+ PB. NISAR: 80 TB/day from a single SAR instrument.
GIS & Cloud Platforms
Drop-in LERC replacement inside Cloud Optimized GeoTIFF. Lower egress, faster tile decode.
Access
Free decoder. Paid encoder.
Decoder
Free
Anyone who needs to read RIPT-encoded data can pull the decoder from the Bitruvius Developer Hub. Compiled binaries for every supported platform; no source.
Encoder + Decoder
Commercial license
Production encoding requires a paid license. Tiers, seats, transactions, and OEM redistribution are listed on the pricing page. All artifacts are compiled binaries.
Compiled-only distribution is intentional: it keeps the patent-pending novelty out of source and protects implementation IP across the supply chain.
Compression
that thinks.
RIPT is the first raster codec that adapts its strategy to every region of every image, across every data type, on every processor.
Esri, ArcGIS, and LERC are trademarks of Environmental Systems Research Institute, Inc., used here solely to identify the products and formats being compared; no affiliation, sponsorship, or endorsement is implied. LERC is published by Esri under the Apache 2.0 license, and the live demo runs Esri's unmodified reference decoder under that license. RIPT, TurboZstd, and TurboLZ4 are independent Bitruvius implementations. Performance comparisons reflect our own measurements under the stated methodology; results vary by workload and hardware. Full third-party notices: Attributions.