GitHub - PearsonZero/kodak-pcd0992-statistical-characterization: Per-image principal component decomposition of the Kodak Lossless True Color Image Suite (PCD0992). First published statistical characterization of inter-channel redundancy structure across all 24 images.

Kodak PCD0992 Statistical Profile Series Per-Image PCA and Inter-Channel Redundancy Analysis of the Kodak Lossless True Color Image Suite Baetzel, J. (2026)

Overview This repository contains the first published per-image statistical characterization of all 24 images in the Kodak Lossless True Color Image Suite (PCD0992). Each image is documented as a two-page reference data sheet reporting the complete inter-channel redundancy structure: covariance matrix, eigendecomposition, Pearson correlations, spatial autocorrelation, and derived classification metrics. All statistics were computed directly from the 8-bit RGB pixel arrays of the standard 768x512 base-resolution PNG distribution. No subjective descriptions appear in any profile. All redundancy classifications are generated programmatically from the computed metrics using fixed thresholds documented in the methodology.

Related Research Parent Paper: Baetzel, J. (2026). Statistical Characterization of Inter-Channel Redundancy Structure in the Kodak Lossless True Color Image Suite. Per-Image Principal Component Decomposition of PCD0992.

Focus: Theoretical framework establishing the first complete per-image PCA decomposition of the Kodak suite. Documents the dimensionality spectrum, blue channel independence range, eigenvector loading patterns, and evidence for deliberate curation across the 24-image collection. Availability: Included in this repository (baetzel_2026_kodak_pca_characterization.pdf).

This Series: Baetzel, J. (2026). Kodak PCD0992 Statistical Profile Series. Per-Image PCA and Inter-Channel Redundancy Analysis.

Focus: Individual reference data sheets and machine-readable metric exports for each of the 24 images. Provides the per-image evidence underlying the suite-wide analysis in the parent paper — covariance matrices, eigendecompositions, correlation heatmaps, spatial autocorrelation, and logic-generated redundancy classifications. Availability: /baseline/ directory (24 PDFs + 25 JSON files).

The parent paper establishes why the Kodak suite spans the full spectrum of inter-channel redundancy. The profile series documents what each individual image contributes to that spectrum.

Dataset Specifications

Property Value

Suite Kodak Lossless True Color Image Suite (PCD0992)

Image Count 24

Resolution 768x512 or 512x768

Bit Depth 24-bit (8 bits per channel)

Color Space sRGB

Color Mode RGB

Format PNG (lossless)

Provenance Kodak PCD Film Scanner 2000, 35mm film, PhotoYCC decode to 8-bit RGB

Computed Metrics Per Image Each two-page profile reports the following: Page 1

RGB channel distribution (smoothed density curves from pixel data) Per-channel statistics: mean, standard deviation, variance, kurtosis, skewness, min, max Inter-channel correlation heatmap (3x3) Pairwise Pearson correlation coefficients (R-G, R-B, G-B) and suite average Full 3x3 covariance matrix

Page 2

Eigendecomposition: eigenvalues, variance explained (%), eigenvector loadings Derived metrics: condition number, eigenvalue ratios, blue channel independence, PC1 dominant channel Dimensionality tier classification Spatial autocorrelation (lag-1, horizontal and vertical) Average local variance (3x3 neighborhood) Redundancy profile (logic-generated from computed metrics)

Suite Overview The 24 images span nearly the full range of inter-channel redundancy configurations achievable through film-based photographic capture. Condition numbers range from 7.55 to 1,739.16 — more than two orders of magnitude — covering color distributions from near-spherical to extremely elongated. Dimensionality Tiers

Tier PC1 Range Count Images

Three-Dimensional (PC1 < 75%) 69.27-73.37% 3 kodim02, kodim03, kodim23

Two-Dimensional (PC1 75-85%) 81.60%

7.55 kodim20: 1,739.16

PC1 Variance kodim03: 69.27% kodim20: 98.42%

Blue Independence kodim15: 2.3% kodim03: 52.0%

Highest Single Pair r

kodim20 R-G: 0.9955

Lowest Single Pair r

kodim03 R-B: 0.2890

How to Read a Profile Sheet Condition Number (lambda1/lambda3): Ratio of the largest to smallest eigenvalue. High values indicate a needle-like color distribution concentrated along one axis. Low values indicate a more spherical distribution where each channel carries independent information. Blue Channel Independence: The percentage of blue channel variance not captured by the first principal component. Computed as (1 - (blue_loading_PC1^2 x lambda1 / Var(B))) x 100. Low values indicate the blue channel is almost entirely predictable from the primary variance axis. High values indicate the blue channel carries substantial unique information. Dimensionality Tier: Classification based on PC1 variance explained. Thresholds at 75%, 85%, 93%, and 97% produce five tiers from Three-Dimensional to Near-Degenerate, corresponding to distinct regimes of inter-channel redundancy. Eigenvector Pattern: The loading structure of the first principal component. Identifies which channel or channel pair drives the dominant variance axis: balanced (all channels near-equal), coupled (two channels co-load), or dominant (one channel leads). Spatial Autocorrelation (lag-1): Pearson correlation between each pixel and its immediate neighbor, computed separately for horizontal and vertical directions. Values near 1.0 indicate smooth, spatially coherent image data.

File Structure / README.md baetzel_2026_kodak_pca_characterization.pdf /baseline/ KODIM01_STATISTICAL_PROFILE.pdf kodim01_stats.json KODIM02_STATISTICAL_PROFILE.pdf kodim02_stats.json ... KODIM24_STATISTICAL_PROFILE.pdf kodim24_stats.json kodak_suite_master_stats.json /docs/ methodology.md

Root: The parent PCA characterization paper and repository README. /baseline/: 24 two-page PDF reference data sheets and 25 JSON files (24 individual + 1 master). /docs/: Computation pipeline documentation for full reproducibility.

References [1] Eastman Kodak Company. Kodak Publication No. PCD-042, 1992. [2] Baetzel, J. (2026). “Statistical Characterization of Inter-Channel Redundancy Structure in the Kodak Lossless True Color Image Suite.” [3] Watanabe, S. “Karhunen-Loeve Expansion and Factor Analysis,” pp. 635-660, 1965. [4] Giorgianni, E.J. and Madden, T.E. Digital Color Management. Addison-Wesley, 1998.

Citation Baetzel, J. (2026). Kodak PCD0992 Statistical Profile Series: Per-Image PCA and Inter-Channel Redundancy Analysis of the Kodak Lossless True Color Image Suite.

License Statistical analysis and profile sheets by Jasmine Baetzel (2026).