zensim

Perceptual image similarity in 22 ms at 1080p. 18x faster than C++ SSIMULACRA2 at 4K.

Built on the same psychovisual foundations as SSIMULACRA2 and butteraugli — multi-scale SSIM, edge artifacts, detail loss, and high-frequency features in XYB color space — but with trained weights, fused SIMD kernels, and multi-threaded computation.

Interactive chart exploration: https://imazen.github.io/zensim/ — scatter zensim / fast-ssim2 / butteraugli against human MOS across CID22 / KADID / TID / AIC corpora, filter by codec + version, with per-band SROCC tables and step-5 (20-bin) breakdowns.

Speed

AMD Ryzen 9 7950X 16C/32T (WSL2), synthetic gradient images, no I/O, pre-allocated buffers. zensim and ssimulacra2-rs use rayon (all cores); C++ libjxl, fast-ssim2, and butteraugli-rs are single-threaded. Enabling rayon for fast-ssim2 and butteraugli-rs made them slower at small sizes due to thread-pool overhead, so they're benchmarked single-threaded. Median of 100 samples via criterion.

SSIMULACRA2 implementations

Resolution	zensim	zensim (1 thread)	C++ libjxl (FFI)	fast-ssim2	ssimulacra2-rs
1280x720	14 ms	39 ms	249 ms	111 ms	545 ms
1920x1080	22 ms	89 ms	377 ms	350 ms	1,056 ms
3840x2160	91 ms	366 ms	1,674 ms	1,364 ms	3,980 ms

Butteraugli implementations (single-threaded)

Resolution	C++ libjxl (FFI)	butteraugli-rs
1280x720	269 ms	83 ms
1920x1080	647 ms	154 ms
3840x2160	2,688 ms	906 ms

Single-threaded zensim is 4x faster than C++ libjxl SSIMULACRA2. Multi-threaded at 4K: 18x.

Reproduce: cargo bench -p zensim-bench --bench bench_compare (C++ libjxl FFI requires a local libjxl build; set LIBJXL_DIR or let the build script auto-clone it)

Correlation with human perception

Full Mohammadi 2025 stat panel against three independent human-rated image quality databases that v0.3 did NOT train on. KADID-10k and TID2013 are excluded because v0.3's recovery-phase-4 retrain included them as training groups — they're no longer fair holdouts. On the CID22 codec-compression holdout, the default profile B reaches SROCC ≈ 0.876 and the deprecated A ≈ 0.86 (fast-ssim2: 0.89), both spending the full 0–100 dial with JND landing near score 60 — the property that matters for codec quality targeting. The detailed per-corpus / per-band tables below are profile A's (the prior default); B's full held-out panel is in benchmarks/profile_b_methodology_2026-07-12.md, and regenerating these tables for B before release is a tracked follow-up.

Higher SROCC + PLCC + KROCC + PWRC is better; lower OR + Z-RMSE is better.

CID22 — codec compression artifacts (n=4,292, sacred holdout)

Metric	SROCC	PLCC	KROCC	OR	PWRC	Z-RMSE
zensim v0.3	0.860	0.853	0.673	0.045	0.909	0.523
fast-ssim2 (SSIMULACRA2)	0.890	0.888	0.706	0.042	0.935	0.460
cvvdp (ColorVideoVDP)	0.821	0.825	0.624	0.042	0.884	0.565
iwssim (Wang & Li 2011)	0.784	0.793	0.594	0.052	0.853	0.610

AIC-3 CTC — JPEG-AIC compression at JND levels (n=600)

Metric	SROCC	PLCC	KROCC	OR	PWRC	Z-RMSE
zensim v0.3	0.776	0.788	0.607	0.042	0.854	0.616
fast-ssim2	0.797	0.809	0.629	0.057	0.872	0.588
cvvdp	0.792	0.803	0.626	0.042	0.866	0.595
iwssim	0.774	0.791	0.606	0.045	0.854	0.612

AIC-4 sample — JPEG-AIC reconstructed JND, 6 codecs (n=300)

Metric	SROCC
zensim v0.3	0.928
fast-ssim2	baselines pending
cvvdp	baselines pending
iwssim	baselines pending

The AIC-4 baseline scores for ssim2/cvvdp/iwssim haven't been folded into our standard panel yet — the raw per-pair metric scores live at /mnt/v/backups/home/work/JPEG-AIC-4-datasets/JPEG-AIC_metric_scores.csv but the SROCC computation against the reconstructed-JND target isn't in the panel doc. v0.3's AIC-4 SROCC of 0.928 is from bake_verdict on the 300-pair val parquet at canonical-2026-05-18/val/aic4.parquet.

CID22 per-band SROCC (10 width-10 bins on the human-MOS scale)

Per CLAUDE.md "10-band reporting rule": the primary release gate is the per-band picture, not the aggregate. Below-PJND bands (B3–B5) are the hard ones because the human-MOS scores in those bands are noisy and bunched. Bands B0–B2 have ≤ 1 sample on CID22 and are omitted.

Band	range	n	v0.3	ssim2	cvvdp	iwssim
B3	[0.30, 0.40)	57	0.051	0.134	0.148	0.096
B4	[0.40, 0.50)	266	0.230	0.289	0.260	0.210
B5	[0.50, 0.60)	615	0.273	0.389	0.290	0.193
B6	[0.60, 0.70)	836	0.287	0.417	0.336	0.210
B7	[0.70, 0.80)	1092	0.408	0.397	0.310	0.283
B8	[0.80, 0.90)	1382	0.500	0.501	0.319	0.413
B9	[0.90, 1.00]	43	0.220	0.112	0.081	0.134

Per-band read: ssim2 wins B5–B6 (where most CID22 mass sits); v0.3 wins B7 (good-quality region) and B9 (near-lossless tail). v0.3 essentially matches ssim2 on B8 (the dominant band). cvvdp + iwssim are weakest across every band — they're stronger as aggregate metrics than per-band rank predictors here.

CID22 per-band Z-RMSE (lower better):

Band	n	v0.3	ssim2	cvvdp	iwssim
B3	57	0.950	0.947	0.990	0.989
B4	266	0.959	0.947	0.962	0.965
B5	615	0.959	0.921	0.954	0.971
B6	836	0.957	0.908	0.941	0.972
B7	1092	0.912	0.907	0.947	0.954
B8	1382	0.866	0.866	0.947	0.909
B9	43	0.937	0.940	0.952	0.854

ssim2 has the tightest Z-RMSE in the mid bands (B4–B6) — this is the ssim2-target training-bias caveat from CLAUDE.md materializing. v0.3 and ssim2 are tied on B7–B8. v0.3 wins the noisy tails (B9 near-lossless).

Per-corpus headline

• CID22: ssim2 wins SROCC by 0.03; v0.3 is second. Note that CLAUDE.md's "SROCC-only verdicts BANNED" caveat applies — older trainers used ssim2-derived targets, which biases SROCC measurements toward ssim2-shaped surfaces. v0.3's Z-RMSE (0.523) trails ssim2's 0.460.
• AIC-3: 4-way tie within 0.02 SROCC; ssim2 nominally best.
• AIC-4: v0.3 SROCC = 0.928. cvvdp / ssim2 / iwssim baselines on AIC-4 haven't been computed into our panel doc yet (raw scores at JPEG-AIC_metric_scores.csv; SROCC against reconstructed-JND target is a TODO).
• None of the four hits all three holdouts — v0.3 trades 0.03 CID22 SROCC for full 0-100 dial coverage + JND@60-bit-exact + per-source PJND tracking (the dial properties that matter for codec targeting). See docs/CODEC_TARGET_METRIC.md.

v0.2 (default-on linear profile through zensim 0.2.x): 228 linear weights × basic+peak features, trained on 218k concordance-filtered synthetic pairs via Nelder-Mead.

A (deprecated since 0.3.0 — the prior default MLP profile, superseded as codec_target() by B below; behind the default-on deprecated-profiles feature): 372-input MLP (372 → 128 → 64 + per-sample-α head + tanh-output pin) with a monotone 7-knot PCHIP dial spline. 27 KB packed bake (v47-strict-QAT, f16 + zerobias, file zensim/weights/v47_strict_qat_native_2026-05-27.bin). Masked-monotone by construction (W1 ≥ 0 on the 300 sign-safe features, rank_w ≤ 0, α ≡ 1): 0 inversions, 0 above-identity, identity = 97.69. Trained on 5 groups (safesyn 196k + cid22_train 17.6k + kadid 10.1k + tid 3k + konjnd_dense 20.2k) via one QAT-native pass. Held-out SROCC: CID22 0.866, KADID 0.793, TID 0.793, KonJND 0.419, AIC-3 0.768, AIC-4 0.885. Methodology: benchmarks/v0_qat_native_methodology_2026-05-27.md.

v0.2 → v0.3 rough score equivalence

Both profiles span 0..100 but use the dial differently. v0.2's linear formula 100 − 18·|d|^0.7 floor-clamps below moderate distortion (38k of 68k cross-codec pairs land at v0.2 ≤ 5, while v0.3 spreads them across 28..50). v0.3 uses the full 0-100 dial with JND landing at exactly score 60. Rough lookup on 68,788 matched cross-codec pairs (Spearman v0.2 ↔ v0.3 = 0.88):

v0.2 target	v0.3 median (p25 → p75)	rough quality region
10	52.6 (47.8 → 55.6)	low-q, dial floor
20	55.2 (52.4 → 58.2)	sub-PJND
30	58.9 (55.0 → 60.5)	approaching JND
40	61.0 (58.9 → 63.6)	just past JND
50	65.8 (64.3 → 66.9)	mid-PJND
60	68.8 (66.4 → 72.5)	comfortable quality
70	78.4 (76.5 → 80.3)	good compression
80	90.0 (87.6 → 91.1)	near-lossless
90	96.6 (95.1 → 97.3)	visually lossless
100	100 (exact, byte-identical short-circuit)	lossless

The mapping is non-linear because v0.2's clamp at low quality compresses the 0-30 range into a single floor; v0.3 differentiates that region. For users targeting "score 70" in v0.2 code, the v0.3 equivalent is roughly score 78.

Reproduce these numbers

Download the datasets (instructions below), then:

# CID22 — expects CID22_validation_set.csv + original/ and compressed/ dirs
cargo run --release -p zensim-validate -- --dataset ./datasets/cid22 --format cid22

# TID2013 — expects mos_with_names.txt + reference_images/ and distorted_images/
cargo run --release -p zensim-validate -- --dataset ./datasets/tid2013 --format tid2013

# KADID-10k — expects dmos.csv + images/
cargo run --release -p zensim-validate -- --dataset ./datasets/kadid10k --format kadid10k

Look for Raw dist corr: SROCC=... in the output — that's the raw distance SROCC reported above. The SROCC (Spearman) line uses mapped scores, which are lower for KADID and TID due to score clamping at 0 (35% of KADID scores clamp).

Quick start

[dependencies]
zensim = "0.3"

use zensim::{Zensim, ZensimProfile, RgbSlice};

// Pick a profile explicitly for pinned reproducibility:
let z = Zensim::new(ZensimProfile::B);
// `B` is the deterministic linear-ensemble default for SDR content; `BHdr`
// is its HDR (absolute-nits) counterpart. Or use
// `ZensimProfile::codec_target()` / `latest_preview()` for the stable
// codec-target contract (both return `B`). `A` (the v47 MLP) is deprecated
// (behind the default-on `deprecated-profiles` feature).
// `src_pixels` / `dst_pixels` are `&[[u8; 3]]` — interleaved, sRGB-encoded
// (gamma, NOT linear) 8-bit RGB. `width`/`height` are `usize`. See "Input
// format" below: getting sRGB-vs-linear wrong silently corrupts every score.
let source = RgbSlice::new(&src_pixels, width, height);
let distorted = RgbSlice::new(&dst_pixels, width, height);
// `compute` returns `Result<ZensimResult, zensim::ZensimError>` — the `?`
// propagates a dimension-mismatch / too-small / too-large error.
let result = z.compute(&source, &distorted)?;
println!("score: {:.2}", result.score()); // 100 = identical, higher = better

Also accepts RgbaSlice (composited over a noise background), imgref::ImgRef (with stride), ZenpixelsSource (with zenpixels feature), and StridedBytes for BGRA, 16-bit, linear float, and wide gamut (Display P3, BT.2020) inputs. See docs.rs for the full ImageSource trait.

Input format (read this — wrong input silently corrupts the score)

The 0..100 score is only meaningful if the pixels you pass match the contract zensim assumes. There is no format auto-detection for the RgbSlice fast path: if you hand it linear bytes where it expects sRGB, or planar bytes where it expects interleaved, it computes a perfectly valid-looking but wrong score — no error is raised. The contract:

• Color encoding: sRGB-encoded (gamma), NOT linear. RgbSlice / RgbaSlice / the Srgb8* and Srgb16Rgba StridedBytes formats all expect display-encoded sRGB values — the bytes a PNG/JPEG decoder gives you. zensim linearizes internally before the XYB conversion. If your data is already linear light, do not feed it as sRGB; use StridedBytes with PixelFormat::LinearF32Rgba (linear 32-bit float RGBA) instead. (Display P3 reuses the sRGB transfer function, so Srgb8* formats linearize it correctly; SDR BT.2020 technically wants BT.1886 — for exact results linearize externally and use LinearF32Rgba. Set primaries via StridedBytes::with_color_primaries.)
• Channel order: interleaved, not planar. RgbSlice takes &[[u8; 3]] laid out R,G,B, R,G,B, … (one [u8; 3] per pixel), RgbaSlice takes &[[u8; 4]] as R,G,B,A, …. Planar input (all R, then all G, then all B) is not accepted by these types — you must interleave it first, or describe it some other way. The [[u8; 3]] / [[u8; 4]] element type also pins it to exactly 3 / 4 bytes per pixel, tightly packed (no per-row padding) — for row padding use StridedBytes (below).
• Dimensions: width and height are usize (not u32). Both RgbSlice::new(data, width, height) and the underlying ImageSource::{width,height} use usize.
• Both images must have identical dimensions, and each must be non-zero. compute returns ZensimError::DimensionMismatch if they differ, ZensimError::ImageTooSmall if either dimension is 0. (Since 0.3.0, sub-64px images down to 1×1 are reflect-padded internally and score normally — only empty inputs are rejected.)

The infallible constructors (RgbSlice::new, RgbaSlice::new, StridedBytes::new) panic if data.len() is too short for width × height (or the stride is invalid). For untrusted sizes use the try_* variants — RgbSlice::try_new(data, width, height) -> Result<RgbSlice, ZensimError> etc. — which return ZensimError::InvalidDataLength / ZensimError::InvalidStride / ZensimError::ImageTooLarge instead of panicking.

Return type and errors

pub fn compute(
    &self,
    source: &impl ImageSource,
    distorted: &impl ImageSource,
) -> Result<ZensimResult, zensim::ZensimError>

ZensimError is a #[non_exhaustive] enum (so match it with a _ arm) — the variants compute can return are DimensionMismatch, ImageTooSmall, and ImageTooLarge (dimensions exceed the configured max_pixels cap — 120 MP by default since #49; tighten it with Zensim::with_max_pixels, or pass with_max_pixels(usize::MAX) to opt out for trusted input — or width × height overflows usize on 32-bit / wasm32). HDR-flagged sources (ImageSource::is_hdr returns true) are refused with HdrInputRequiresPuPath — score HDR via the PU21 front-end (Zensim::compute_pu_linear, fed absolute-luminance linear RGB in cd/m²) instead. On success, ZensimResult::score() is the 0..100 similarity; raw_distance(), approx_ssim2(), approx_dssim(), and approx_butteraugli() are also available (see "What the score means").

Strided / padded rows

When rows are not tightly packed (SIMD-aligned padding, a sub-region crop of a larger buffer, decoder output with row guards), use StridedBytes, where stride is the byte distance between the start of consecutive rows:

use zensim::{StridedBytes, PixelFormat};

// e.g. 8-bit RGB where each row is padded to `row_stride` bytes (≥ width*3):
let src = StridedBytes::new(&bytes, width, height, row_stride, PixelFormat::Srgb8Rgb);
// `try_new(..) -> Result<_, ZensimError>` returns InvalidStride / InvalidDataLength
// instead of panicking. `with_alpha_mode(.., AlphaMode)` / `with_color_primaries(..)`
// set alpha handling and gamut; default alpha mode is `AlphaMode::Unknown`.
let result = z.compute(&src, &dst)?;

With the imgref feature (on by default), imgref::ImgRef<'_, rgb::Rgb<u8>> and ImgRef<'_, rgb::Rgba<u8>> implement ImageSource directly and honor their pixel stride — pass an ImgRef straight to compute. (The element type must be rgb::Rgb<u8> / rgb::Rgba<u8>; the ImgRef stride is in pixels.)

Cancellation

A single compute / compute_with_ref call is not interruptible mid-computation — the zensim metric library exposes no Stop-token parameter, and a single comparison is typically tens of milliseconds (~22 ms at 1080p). Cancellation is at the granularity of your loop: when comparing one reference against many distorted variants (see "Batch comparison"), check your own cancellation flag between compute_with_ref calls. The enough cooperative-cancellation crate is used by the zensim-target codec-targeting CLI (to bound its binary-search loop) and zensim-regress, not by the core metric API.

zenpixels integration

With the zenpixels feature, pass any PixelSlice or PixelBuffer directly:

[dependencies]
zensim = { version = "0.3", features = ["zenpixels"] }

use zensim::{Zensim, ZensimProfile, ZenpixelsSource};

let source = ZenpixelsSource::try_from_slice(&pixel_slice)?;
let distorted = ZenpixelsSource::try_from_slice(&other_slice)?;
let result = Zensim::new(ZensimProfile::codec_target()).compute(&source, &distorted)?;

Format mapping is automatic: RGBX/BGRX becomes opaque, premultiplied alpha is un-premultiplied, color primaries are forwarded. HDR (PQ, HLG) and grayscale are rejected with UnsupportedFormat.

Target-score CLI (zensim-target)

The zensim-target workspace crate is the runtime side of the "user-facing quality dial" goal. Given an input image and a target zensim score, it picks the codec quality knob via binary search:

cargo run --release -p zensim-target -- input.png \
    --target 70 --codec zenjpeg --output out.jpg
# codec=Jpeg  target=70.0  achieved=69.46  knob=78.44  bytes=62234  iters=5  converged=true

Supported codecs: zenjpeg, zenwebp, zenavif (wired and demonstrated); zenpng (lossless, single probe); zenjxl (encode-only in v0.1, decode plumbing pending). Demo matrix at benchmarks/zensim_target_demo_2026-05-18.md: 33 / 36 cells converged within ±1.5 score units, median 5 iterations.

zensim-target is AGPL-3.0-only because it links the AGPL zen codec crates; the core zensim library stays MIT/Apache.

What the score means

100 = identical. Higher = more similar. Every published profile (A, B, BHdr) routes its raw MLP/linear-ensemble output through a monotone PCHIP dial spline calibrated so the dial tracks degradation monotonically (identity ≈ 97.7 for A; byte-identical inputs short-circuit to exactly 100 for all profiles).

Each ZensimResult also provides approximate translations to other metrics:

Method	What it returns
score()	Zensim similarity (0-100)
raw_distance()	Feature distance before mapping (lower = better)
approx_ssim2()	SSIMULACRA2 estimate (MAE 4.4 pts, Pearson r = 0.974)
approx_dssim()	DSSIM estimate (MAE 0.00129, Pearson r = 0.952)
approx_butteraugli()	Butteraugli estimate (MAE 1.65, Pearson r = 0.713)

The mapping module has bidirectional interpolation tables — including JPEG quality. These are median values from 344k synthetic pairs across 6 codecs (source: zensim/src/mapping.rs):

Zensim	≈ SSIM2	≈ DSSIM	≈ JPEG quality
98	96.50	0.000017	~q95
90	89.41	0.000278	~q60
80	80.51	0.001119	~q30
70	71.40	0.002356	—

JPEG quality mapping accuracy is ±7 quality units MAE — individual images vary widely.

Regression testing

zensim-regress tracks pixel output across platforms and dependency updates. Hash-based checksums for fast exact matches; perceptual comparison with forensic evidence when hashes diverge. Amplified diff images, error classification, architecture-specific tolerances, CI manifests, and HTML reports.

use zensim_regress::checksums::{ChecksumManager, CheckResult};

let mgr = ChecksumManager::new("tests/checksums".as_ref());
let result = mgr.check_pixels("resize", "bicubic", "200x200",
    &pixels, width, height, None).unwrap();
assert!(result.passed(), "{result}");

Run with UPDATE_CHECKSUMS=1 to create baselines. See the zensim-regress guide for the full workflow.

Batch comparison

Compare one reference against many distorted variants. Precomputing the reference skips redundant XYB conversion and pyramid construction — saves ~25% per comparison at 4K.

let precomputed = z.precompute_reference(&source)?;
for dst_pixels in &distorted_images {
    let dst = RgbSlice::new(dst_pixels, width, height);
    let result = z.compute_with_ref(&precomputed, &dst)?;
}

How it works

228 features — 19 per channel (X, Y, B) per scale (1x, 2x, 4x, 8x) — scored by trained weights:

• SSIM (mean, L2, L4 pooling) — structural similarity in XYB, using ssimulacra2's modified formula (no luminance denominator)
• Edge artifacts (mean, L2, L4) — ringing, banding, blockiness
• Detail loss (mean, L2, L4) — blur, smoothing, texture destruction
• MSE in XYB color space
• High-frequency features — energy loss, magnitude loss, energy gain
• Peak features — per-feature max and L8-pooled (near-worst-case)

Computed in XYB (cube-root LMS) with O(1)-per-pixel box blur and fused AVX2/AVX-512 SIMD kernels via archmage. Safe scalar fallback on all platforms.

Profiles

Each ZensimProfile bundles weights and score-mapping parameters. Scores from a given profile stay stable across crate versions. The published crate ships five selectable profiles (A deprecated, PreviewV0_1 / PreviewV0_2 retained for 0.2.7 compatibility, B / BHdr current); the historical / experimental research profiles are preserved (bit-identically) in the unpublished zensim-experimental crate.

Profile	Kind	CID22 SROCC	Bake
B (default — codec_target())	372-input linear ensemble (35-weight lasso) + dial spline, SDR content	0.8764	7.3 KB ens-Pline-cid80
BHdr	linear ensemble on PU-linear (absolute-nits) features + dial spline, HDR content only	n/a — HDR-only (UPIQ-HDR |SROCC| 0.7313)	11.7 KB hdr-lasso0.001-shaped
A (deprecated — behind deprecated-profiles)	372-input MLP, per-sample-α + monotone PCHIP dial spline	0.8657	27 KB v47-strict-QAT
PreviewV0_1 / PreviewV0_2	228-weight linear (no MLP), 100 − 18·d^0.7 mapping — 0.2.7-compat	—	embedded weight arrays

ZensimProfile::codec_target() and latest_preview() both return B — the canonical production codec-target the zen codecs dial against (the deprecated latest() also returns B). A (the prior default, the v47 MLP) is now #[deprecated] and lives behind the default-on deprecated-profiles feature — build with --no-default-features to drop it. To load your own bake, construct ZensimProfile::Custom { params, name } via ProfileParams::builder(). Results are deterministic for the same input on the same architecture; cross-architecture scores (AVX2 vs scalar vs AVX-512) may differ by small ULP.

ZensimProfile::PreviewV0_1 / PreviewV0_2 (the linear profiles that shipped in 0.2.7) are RETAINED as first-class, non-deprecated, selectable variants — the 0.3.0 line reverted their removal (commit 493c91cd) to preserve semver compatibility with 0.2.7; see the CHANGELOG [Unreleased] Restored entry. B is the current deterministic-linear default for SDR content.

The historical PreviewV0_4 / PreviewV0_5* SOTA-trail variants, A_Phone, and LinearBounded live in the zensim-experimental crate (not published), each rebuilt through the Custom extension point — e.g. Zensim::new(zensim_experimental::preview_v0_5_tuner_v4()). zenpredict (the MLP runtime) is MIT/Apache-2.0 — no AGPL transitive obligation on default builds.

Feature flags

Flag	Default	Description
avx512	yes	AVX-512 SIMD paths
threads	yes	Multi-threaded computation via rayon (disable for wasm / single-threaded)
imgref	yes	ImageSource impls for imgref::ImgRef<Rgb<u8>> and ImgRef<Rgba<u8>>
training	no	Expose metric internals for weight training
classification	no	Error classification API (classify(), DeltaStats, ErrorCategory)
zenpixels	no	ImageSource adapter for zenpixels PixelSlice/PixelBuffer
custom-profiles	no	ZensimProfile::Custom + ProfileParams::builder() for externally-defined bakes
streaming_strips_oom	no	Un-ignores the ~500 MB 80 MP streaming OOM-relief integration test

Downloading evaluation datasets

To reproduce the SROCC numbers above, you need the three human-rated datasets. All are freely available for research use.

TID2013 — ponomarenko.info/tid2013.htm

25 reference images, 3,000 distorted (24 distortion types × 5 levels). Download the RAR archive, extract so you have mos_with_names.txt, reference_images/, and distorted_images/ in the same directory.

N. Ponomarenko et al., "Image database TID2013: Peculiarities, results and perspectives," Signal Processing: Image Communication, 2015. DOI: 10.1016/j.image.2014.10.009

KADID-10k — database.mmsp-kn.de/kadid-10k-database.html

81 reference images, 10,125 distorted (25 distortion types × 5 levels). Download from OSF. Expected structure: dmos.csv and images/ directory in the same parent.

H. Lin, V. Hosu, D. Saupe, "KADID-10k: A Large-scale Artificially Distorted IQA Database," QoMEX 2019. DOI: 10.1109/QoMEX.2019.8743252

CID22 — cloudinary.com/labs/cid22

49 validation reference images, 4,292 distorted (6 codecs, medium-to-lossless quality). Download the validation set. Expected structure: CID22_validation_set.csv, original/, and compressed/ in the same directory. CC BY-SA 4.0.

Jon Sneyers et al., "CID22: A Large-Scale Subjective Quality Assessment for Lossy Image Compression," 2024.

Workspace

Crate	Description
zensim	Metric library
zensim-regress	Visual regression testing (guide)
zensim-experimental	Historical / research profiles via the Custom extension point (unpublished)
zensim-validate	Evaluation and training CLI (internal)
zensim-bench	Comparative benchmarks (standalone root, sibling-dep)
zensim-target	Target-score codec CLI (standalone root, AGPL, sibling-dep)

MSRV

Rust 1.93.0 (2024 edition).

License

MIT OR Apache-2.0

AI-Generated Code Notice

Developed with Claude (Anthropic). Not all code manually reviewed. Review critical paths before production use.

Image tech I maintain

Codecs ¹	zenjpeg · zenpng · zenwebp · zengif · zenavif · zenjxl · zenbitmaps · heic · zentiff · zenpdf · zensvg · zenjp2 · zenraw · ultrahdr
Codec internals	zenjxl-decoder · jxl-encoder · zenrav1e · rav1d-safe · zenavif-parse · zenavif-serialize
Compression	zenflate · zenzop · zenzstd
Processing	zenresize · zenquant · zenblend · zenfilters · zensally · zentone
Pixels & color	zenpixels · zenpixels-convert · linear-srgb · garb
Pipeline & framework	zenpipe · zencodec · zencodecs · zenlayout · zennode · zenwasm · zentract
Metrics	zensim · fast-ssim2 · butteraugli · zenmetrics · resamplescope-rs
Pickers & ML	zenanalyze · zenpredict · zenpicker
Products	Imageflow image engine (.NET · Node · Go) · Imageflow Server · ImageResizer (C#)

_{¹ pure-Rust, #![forbid(unsafe_code)] codecs, as of 2026}

General Rust awesomeness

zenbench · archmage · magetypes · enough · whereat · cargo-copter

Open source · @imazen · @lilith · lib.rs/~lilith