Measured reference results

The configured protocol contains 569 cases and 1117 measured runs. All tables link to recorded evidence. See measurement contracts for units, assumptions, uncertainty and limits, and the explorer for field-specific comparisons.

Field	Recorded runs
Information bottleneck	81
Channel capacity	51
Noisy communication	120
Information estimation	324
Entropy and dependence	21
Real image compression	96
Lossless compression	154
Learned information estimators	54
Scalar quantization	48
Rate and distortion	90
Source–channel transmission	30
Sources and typicality	25
Rare events and hypothesis tests	23

Selected findings

These numerical examples show practical behavior; they are not universal method rankings.

At 0 dB and seven channel uses per Gaussian source symbol, analog MSE is 0.37481; digital Hamming soft-ML MSE is 3.84462, and source-prior posterior-mean MSE is 1.04249. The matched-use power budget makes this a meaningful finite-code comparison.
For 16 dimensions and correlation 0.95, Gaussian MI is 26.86763 bits. InfoNCE estimates range from 7.99554 to 7.99699 bits across three seeds, approaching the 8-bit batch ceiling rather than the truth. MINE-style estimates and strong Gaussian/KSG baselines are available alongside this result.
At binomial blocklength 500, naive simulation observes 0 tail hits in 10000 trials while the exact natural-log probability is -79.78167. Importance sampling estimates the same rare probability with reported relative uncertainty.

Image codec examples

Four attributed photographs, complete actual containers, RGB8 decoded-pixel metrics, CPU timing medians. Pretrained model weights are shared external inputs and not amortized per image. This small resized set supports a demonstration, not a benchmark or SOTA claim.

Coffee codec	Point	Complete bpp	PSNR dB	MS-SSIM	Decode seconds
avif	35	0.37817	29.585	0.96446	0.00644
avif	60	1.05558	34.415	0.98825	0.00801
avif	85	2.13761	38.580	0.99481	0.01247
bmshj2018-factorized	1	0.15999	26.539	0.91642	0.13094
bmshj2018-factorized	3	0.30843	28.456	0.95507	0.14885
bmshj2018-factorized	5	0.65348	30.851	0.97883	0.14642
bmshj2018-hyperprior	1	0.16764	26.799	0.91979	0.15888
bmshj2018-hyperprior	3	0.35124	29.391	0.96006	0.11761
bmshj2018-hyperprior	5	0.71712	32.222	0.98280	0.11789
cheng2020-anchor	1	0.13932	27.713	0.92808	1.47211
cheng2020-anchor	3	0.27799	29.887	0.96172	1.48661
cheng2020-anchor	5	0.61719	33.434	0.98405	2.26499
jpeg	35	0.95955	29.538	0.96814	0.00071
jpeg	60	1.45036	31.408	0.98169	0.00095
jpeg	85	2.68734	35.105	0.99251	0.00117
jpeg2000	20	1.19979	30.846	0.96281	0.01160
jpeg2000	10	2.39811	34.947	0.98443	0.01623
jpeg2000	5	4.79883	41.314	0.99641	0.02493
mbt2018-mean	1	0.15592	26.808	0.92029	0.12452
mbt2018-mean	3	0.33105	29.587	0.96042	0.12407
mbt2018-mean	5	0.72298	33.431	0.98368	0.22090
webp	35	0.62061	30.022	0.96284	0.00150
webp	60	0.91976	31.847	0.97517	0.00198
webp	85	1.72363	34.884	0.98733	0.00198

All scalar metrics are in comparison.csv, full row/asset associations in results.json, and independent validation in reference_validation.json. Exact numeric quality settings differ between codec families; compare rate and decoded quality.