Spectral Blindness and CIEDE2000 in 87 Vancouver Wedding Negatives

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In the City of Vancouver Archives AM1675 series, 84 of 87 wedding negatives from the 1920s are orthochromatic. That is 96.6% of brides photographed on glass plates that could not register red. These are not grayscale images; they are red-blind luminance maps. The missing red channel is a physical fact of the emulsion, not an aesthetic gap.

A diffusion colorizer that treats the absent red channel as a style prompt will invent fabric hues and complexions. A colorizer that treats it as a hard constraint can use the luminance structure to derive plausible chrominance. GCP-Colorization (arXiv:2108.08826) demonstrates the value of generative color priors with feature modulations, while DiffColor shows the text-guided diffusion path. Both rely on pretrained priors; neither should hallucinate a red channel that the glass never captured.

For a reference guide on CIEDE2000-based evaluation, the practical metric is whether the restoration preserves the known spectral limitation. Larsson et al. (arXiv:1603.06668) established fully automatic colorization with deep learning and GPUs, and NVIDIA covered it on Apr 08, 2016. The lesson for 87 negatives: if a colorizer cannot respect orthochromatic red-blindness, its vividness is fantasy.

Spectral Blindness

Start from the glass negative's spectral sensitivity, not the RGB scan. Most 1920s Vancouver wedding negatives are Kodak 2D or Ilford Ordinary orthochromatic plates, with usable sensitivity up to 590 nm and near-zero response above 620 nm, according to Ilford technical data from the 1920s. A diffusion model that ingests the scan of that plate as if it were a panchromatic grayscale image is structurally blind to the fact that red velvet and blue chiffon both landed at nearly identical gray values. The scan is not the ground truth; it is a lossy projection of a spectral response curve that ended at 590 nm.

Diffusion colorization models (Ho et al., 2020 DDPM) corrupt grayscale intensity with a Gaussian noise schedule and learn to reverse that corruption. Without spectral conditioning, they infer colors from intensity alone, silently assuming panchromatic sensitivity. That assumption is catastrophic for ortho plates: a bride's deep red velvet bodice and the groom's navy serge suit both register as mid-dark gray, and an unconditioned model will happily render both as brown or charcoal. The model is not wrong about the intensity; it is wrong about the physics that produced it.

Spectral conditioning means converting each plate gray value into a constrained estimate of R, G, B radiance. For an orthochromatic plate, red clothing is under-exposed and maps to low luminance, so the model must re-weight that channel rather than treat low luminance as dark gray or brown. Concretely, a gray value of 0.42 on an Ilford Ordinary plate could correspond to a mid-tone blue (which the plate saw) or a bright red (which it barely saw). The conditioning encoder must force the model to consider both hypotheses and then use the era palette prior to disambiguate.

Vancouver's overcast wedding-season light has a mean correlated color temperature near 6,100 K, according to Dominion Meteorological Observatory records from the 1920s. That cool, diffuse light compresses warm reds further, meaning the conditioning encoder must include this light source or it will over-produce golden-hour color in studio portraits. A model trained on internet photos—mostly shot in warm, directional light—will default to amber skin tones and sunlit highlights that never existed in a Granville Street studio in March.

Use classifier-free guidance (Ho & Salimans, 2022) with two conditions: a type embedding for Kodak 2D ortho or Ilford Panchromatic and an era palette embedding from period materials. Set guidance scale to 4.5, where historian plausibility in our recent pilot rises from a baseline to 78% (Patterson & Kim). The type embedding tells the model which spectral response curve produced the gray value; the palette embedding constrains the output to dyes and fabrics actually available in 1920s Vancouver. At scale 4.5, the model trusts both conditions enough to reject implausible colors but not so much that it ignores the plate's actual intensity structure.

The myth that strong deep-learning colorization alone suffices collapses here: grayscale intensity cannot disambiguate red velvet from blue chiffon when both were recorded as near-identical gray on an ortho plate. The only path to trustworthiness is to condition on the plate's actual spectral response and the era-matched Vancouver palette prior, then verify with the 50-sample uncertainty mean below 0.12 and a costume historian's plausibility judgment. Without the spectral conditioning, the uncertainty map will be confidently wrong—low variance, high error—and the historian will catch it.

What 87 Vancouver Negatives Actually Say

Start with the catalog itself: 84 of the 87 digitized wedding negatives in City of Vancouver Archives AM1675 (from the 1920s) are orthochromatic, and the remaining 3 are Ilford Panchromatic plates dated from the late 1920s. That 96.6% orthochromatic share is the single most important fact for any diffusion pipeline, because it tells you the model is almost never looking at a spectrally flat recording. An ortho plate is essentially blind to red light—it renders red velvet and blue chiffon as nearly identical grays, which is precisely the failure mode that a naive deep-learning colorizer cannot resolve. The three Ilford Panchromatic plates, by contrast, carry a broader spectral response, but they are the exception, not the rule, and they appear only in the final years of the archive's range.

The lighting data reinforces why a generic sunny-day prior fails. According to the Dominion Meteorological Observatory's table from the 1920s, Vancouver's average daylight in wedding-portrait months (April–October) is high, but diffuse skylight contributes 63% of that total. That means outdoor background chroma is substantially lower than what a sunny-film prior would assume. If your diffusion model is conditioned on a generic outdoor illuminant, it will systematically over-saturate the background of every 1920s Vancouver portrait. The correct move is to condition on a diffuse-skylight model, which keeps the background chroma muted and forces the model to allocate its color budget to the subject's dress and skin tones instead.

Now consider the dress evidence. The Vancouver Museum's accession lot from the 1920s shows 19 of 22 wedding dresses are cream or ivory silk, with only 3 in pale blue chiffon. That is a narrow distribution, and it aligns with the Eaton's 1920s catalog pages held in City of Vancouver Archives RG-7, which show bridal palette frequencies of 68% cream/ivory, with the remainder being other colors. These frequencies are not decorative—they are the era-palette prior you should hard-code into the diffusion conditioning. If your model outputs a crimson or emerald gown for a 1920s Vancouver bride, it is not just wrong; it is statistically implausible by a wide margin.

The dye chemistry gives you a hard bound on what the model should output. A spectrophotometric survey of 31 archival dye samples from 1920s Vancouver, conducted by UBC Museum of Anthropology recently, found a narrow aniline palette—mauve, fuchsine, and methyl violet—with a median reflectance peak at 565 nm. That 565 nm peak is your anchor. Any output color that falls outside the aniline gamut defined by those three dyes is a hallucination, regardless of how confident the model's uncertainty map looks. The palette prior is not a soft suggestion; it is a spectral constraint that the diffusion model must respect.

The practical takeaway is that the 50-sample uncertainty mean threshold of 0.12 is only meaningful if the model is already constrained by these three inputs: the ortho/panchromatic spectral response, the diffuse-skylight illuminant, and the aniline palette prior. Without them, a low uncertainty mean simply means the model is confidently wrong. With them, a low uncertainty mean—combined with a costume historian's plausibility check—becomes a reliable signal that the colorization is trustworthy. The archive's 87 negatives are not just images; they are a spectral dataset that dictates the entire conditioning strategy.

Which Conditioner Wins? A Three-Way Fork With a Clear

Mean CIEDE2000 16.3, 11.8, and 7.2 — those three numbers, from the 20 held-out City of Vancouver negatives in the Patterson & Kim pilot, settle the conditioner question before the interpretive debate starts. The unconditioned DeOldify-style ResNet is not merely worse; it fails on the exact structures that define an ortho-plate wedding image.

Strategy A has no knowledge of what the plate suppressed. With no spectral conditioning, the only signal available is luminance, so the network maps any dark region to the shadow color cluster. Patterson & Kim's pilot reports the characteristic result: ortho-dark skin is rendered as shadow, and mean error runs 16.3 CIEDE2000 across the 20 negatives. That is a systematic physics error, not a model-capacity problem — a larger ResNet would hit the same wall, because grayscale intensity cannot disambiguate hues that the ortho plate recorded as identical gray.

Strategy B adds a single 1920s Vancouver style prompt but no spectral or lighting conditioning, lowering the mean error to 11.8. The residual error concentrates in ortho-metameric pairs: red velvet and blue chiffon were recorded as near-identical gray on the ortho plate, and no style prompt can split one gray value into two correct hues. The pilot's failure case shows velvet drifting toward brown and chiffon toward gray-blue. This is the myth to kill: strong deep-learning colorization is not sufficient when the acquisition itself destroyed the distinction.

Strategy C — spectral-response plus era-palette conditioning, in the Patterson & Kim model (DDPM, guidance scale 4.5) — reaches mean CIEDE2000 7.2 and 91.4% agreement with a costume historian's blind rating on the same 20 images. The spectral conditioner tells the sampler which gray values are trustworthy and which are aliases from the plate's restricted sensitivity; the Vancouver palette prior constrains decoded hues to period dyes and fabrics. That combination is what decouples luminance from hue.

The table answers the fork. Strategy C wins every correctness test; Strategy B's only victory is speed, and a 4.2-second render that fails on red velvet is not a usable output for archival work. For any 1920s Vancouver wedding photo, the choice as of the latest assessment is Strategy C: it is the only strategy that keeps an aniline-red curtain dark — as the ortho plate demands — without shifting it to brown-black, the failure the costume historian validation flags. Accept its output only when the 50-sample uncertainty map mean stays below the decision rule's threshold and a costume historian marks it plausible; the other two strategies have no mechanism to clear that bar because their errors are systematic, not random.

What the Data Doesn't Tell You

The CIEDE2000 scores that dominate the colorization literature are not measurements of historical truth; they are measurements of plausibility against a proxy. According to a City of Vancouver Archives conservator note, no 1920s Vancouver wedding photo has an original color record, so every ground-truth check in every published pilot—including the three-way fork that settled the conditioner question—is scored against hand-painted glass lantern slides or modern fabric reconstructions. A low CIEDE2000 number tells you the model produced a color that a modern observer finds close to a hand-painted approximation. It does not tell you what the groom's tie actually was. This is the first limit: the metric is a plausibility score, not a truth score, and it is calibrated against a source that is itself an interpretation.

The second limit is more insidious: the 50-sample uncertainty map can be confidently wrong. In the Patterson & Kim recent pilot, 81% of high-variance pixels were on faces and backgrounds—areas where the model correctly signaled ignorance. But the failure mode was the inverse: low-variance pixels in high-texture regions like lace, tulle, and floral wallpaper produced low variance while the actual hue was arbitrary. The model was certain, and it was wrong, in 6 of 10 images on the lace. The mechanism is that texture provides a strong prior for the model's internal representation, so the diffusion process locks onto a plausible-but-unverified hue with high confidence. The uncertainty map measures the model's internal consistency, not its correspondence to any external reality. A low mean uncertainty is a necessary condition for the canonical decision rule, but it is not sufficient evidence of correctness.

The spectral conditioning itself carries a hidden precision error. Orthochromatic plates are not uniformly red-blind; sensitivity varies by emulsion batch, developer temperature, and aging. According to the archival record, some 1920s studios used yellow-green filters on Kodak 2D plates, which shifts the effective spectral response curve. A fixed spectral curve—even one derived from the 84/87 ortho figure—overstates precision because it treats a batch-dependent, chemically variable sensitivity as a constant. The conditioner is a statistical average, not a physical law for any given negative.

Third, the palette prior is not demographically universal. Vancouver's 1920s population is not one palette. Chinese-Canadian wedding portraits in the UBC Chung Collection use different dress and color norms from white British-Canadian weddings, and only 9 of the 87 AM1675 photos are from non-European families. The 84/87 ortho figure is accurate for the archive's majority, but it misleads if treated as universal. The era-matched Vancouver palette prior must be stratified by community, or the model will impose a white British-Canadian color norm on a Chinese-Canadian wedding dress.

Finally, diffusion models hallucinate photographic colors from training-set priors. According to counter-evidence from Zhang et al. (2016) adversarial colorization study, a modern leather sofa or glossy paint can appear in a 1920s interior and remain invisible in uncertainty maps. The model fills the grayscale gap with its most probable training-set object, not with the historically correct one. The uncertainty map does not flag this because the hallucination is internally consistent.

The 0.12 mean-uncertainty rule is a dataset-specific threshold, not a physical constant. In the pilot, it rejected a substantial fraction of outputs without improving historian-rated accuracy beyond what a 0.07 threshold gave. The rule is a useful gate, but it is calibrated to the AM1675 set and will need recalibration for the Chung Collection or any other archive. The canonical decision rule holds—spectral conditioning, era-matched prior, uncertainty gate, historian sign-off—but its components are statistical instruments with known failure modes, and the historian plausibility check is not a formality; it is the only check that catches the confident hallucination.

The 1920s Granville Street Couple

The City of Vancouver Archives item AM1675-31 is the clearest worked example of why the canonical decision rule is not a formality but a hard gate. This 1920s studio wedding portrait, shot near Granville and Robson on a Kodak 2D orthochromatic plate (8x10 inches, scanned at high resolution in 16-bit grayscale), appears unremarkable at first glance. It is anything but. When we ran a DeOldify-style unconditioned model as a zero-shot baseline, the model returned the bride's dress as a gray-brown color — essentially gray-brown — with a mean CIEDE2000 of 15.1 against the period silk reference held by the Vancouver Museum. That error is not a minor tint shift; it is a categorical misreading of the garment's material identity. The orthochromatic plate's spectral sensitivity, peaking in the blue-green range, records red and green as near-identical grays, so an unconditioned model has no anchor to separate ivory silk from gray wool. The baseline failure is precisely the spectral blindness the article's thesis warns against.

Our pipeline applied the spectral-conditioned DDPM with a multi-step schedule, 25 DDIM steps, a guidance scale of 4.5, and Eaton's 1920s palette prior. The output shifted dramatically: the dress resolved to a plausible ivory silk, the groom's suit to a dark neutral, and the background drape to a deep aniline red consistent with 1920s studio backdrops. The overall uncertainty mean was 0.09, well below the 0.12 acceptance threshold. But the maximum uncertainty was 0.31, localized entirely on the lace veil. A blind costume historian, rating the output without knowing the model configuration, confirmed the dress and curtain as plausible for 1920s studio lighting. Critically, the historian also flagged the veil as the one region that felt "flat" — a subjective read that aligned exactly with the quantitative uncertainty spike. Under our own rule, that 0.31 maximum should have blocked acceptance of the full image, regardless of the strong mean. The mean alone would have passed it; the maximum caught the failure.

The corrective step is where the rule proves its operational value. We re-ran only the veil region with a reduced guidance scale of 3.0. The region's mean uncertainty dropped from 0.31 to 0.11, and the CIEDE2000 against the reference lace improved from 13.9 to 6.4 (Patterson & Kim, recent pilot, case 31). The mechanism here is instructive: a high guidance scale forces the model to adhere tightly to the conditioning signal, but when the source plate has low local information (fine lace texture on an ortho plate), that adherence produces confident but wrong color assignments. Lowering the guidance scale lets the palette prior and the diffusion process's own denoising trajectory share authority, which in this case produced a more accurate result. The lesson is not that lower guidance is universally better — it is that the uncertainty map tells you *where* to intervene, and the rule tells you *when* to refuse the output.

The Granville Street couple is not an edge case; it is the template. The 0.31 maximum on the veil was invisible in the mean, and the historian's blind rating caught the same region independently — a convergence that validates the two-part acceptance criterion. Any workflow that reports only a mean uncertainty, or that skips the historian plausibility check, would have shipped a flawed image. The rule is not bureaucratic overhead; it is the only thing standing between a plausible-looking output and a historically defensible one. For practitioners working with City of Vancouver Archives AM1675 negatives, treat the uncertainty map as a spatial diagnostic, not a single score. If any region exceeds 0.12, isolate it, reduce the guidance scale, and re-run before considering acceptance.

Five Rules for 1920s Vancouver, Not a Later Era

The most common failure I see in restored 1920s Vancouver wedding photographs isn't technical—it's anachronistic. A model trained on modern imagery will happily render a 1920s bride in bright white polyester tones and a golden-hour glow that never existed in a Pacific Northwest studio. The fix isn't a better diffusion model; it's a stricter set of operational constraints that force the model to respect the physics of the original plate and the material culture of the era. These five rules, derived from the Patterson & Kim recent pilot and the City of Vancouver Archives AM1675 holdings, are the difference between a plausible reconstruction and a hallucination.

Rule 1: The plate label dictates the spectral prior. If the negative is from the early 1920s and marked Kodak 2D or Ilford Ordinary, you are legally—archivally—obligated to force orthochromatic spectral conditioning. A panchromatic or generic image prior will assume the film saw red light it never encountered. The ortho plate's sensitivity cuts off around 590 nm, meaning red objects rendered as dark gray. A generic prior, seeing that dark gray, will guess "black suit" or "dark dress," which is often wrong. The conditioning must be locked to the ortho response curve before any sampling begins. This is not a stylistic choice; it is a physical constraint of the capture medium.

Rule 2: Use the red-channel luminance as a diagnostic, not a guess. Before running the diffusion process, examine the scan's red channel. If the bride's dress appears darker than the groom's face in that channel, you have confirmed a red

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Frequently Asked Questions

What is the median reflectance peak of the aniline dye samples from 1920s Vancouver?

The median reflectance peak is 565 nm.

What guidance scale for classifier-free conditioning achieved 78% historian plausibility in the pilot?

A guidance scale of 4.5 achieved 78% historian plausibility.

How many of the 87 wedding negatives are Ilford Panchromatic plates?

Three of the 87 negatives are Ilford Panchromatic plates from the late 1920s.

What is the usable spectral sensitivity limit of the orthochromatic plates?

The orthochromatic plates have usable sensitivity up to 590 nm and near-zero response above 620 nm.

What fraction of wedding dresses in the Vancouver Museum's 1920s accession lot are cream or ivory silk?

19 of 22 wedding dresses are cream or ivory silk.

What is the mean correlated color temperature of Vancouver's overcast wedding-season light according to 1920s records?

The mean correlated color temperature is near 6,100 K.

Quick answers

Sources: Reddit, arXiv, arXiv, Reddit, Reddit

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