We present an empirical case study of cinema SDR-to-HDR mapping using ASC StEM2, a rare common-source dataset containing EXR scene-referred images and matched SDR/HDR cinema release masters from the same ACES-based mastering workflow. Based on pixel-wise statistics over all 18,580 frames of the test film, we construct a three-domain comparison involving EXR source data, SDR release masters, and HDR release masters to characterize their luminance and color structural relationships within this controlled workflow. In the luminance dimension, SDR and HDR masters exhibit a highly stable global monotonic correspondence, with geometric structure remaining largely consistent overall; sparse and structured deviations appear in self-luminous highlights and specific material regions. In the color dimension, the two masters remain largely consistent in hue, with saturation exhibiting a redistribution pattern of shadow suppression, midtone expansion, and highlight convergence. Using EXR as a scene-referred anchor, we further define a pixel-level decision map that operationally separates EXR-closer recovery regions from content-adaptive adjustment regions. Under this operational definition, 82.4% of sampled image regions are classified as EXR-closer recovery, while the remainder require localized adaptive adjustment. Rather than claiming a universal law for all cinema mastering pipelines, the study provides an interpretable quantitative baseline for structure-aware SDR-to-HDR analysis and for designing learning-based models under shared-source mastering conditions.
Figures & tables
Figure 1: Timeline of StEM2.
Figure 2: Global mapping relationship between SDR and HDR masters in the luminance domain.
Scene
Number of Shots
Mean ( R2 )
Minimum ( R2 )
Gradient Correlation ( ρ )
Day Car Interior
57
0.9995
0.9927
0.963
Night Car Interior
21
0.9993
0.9966
0.980
Cave
39
0.9963
0.9169
0.944
Desert
32
0.9990
0.9795
0.970
Hybrid VFX
10
0.9986
0.9942
0.970
Night Interior
40
0.9995
0.9965
0.980
Table 1: StEM2 SDR-HDR luminance mapping statistics by scene.
Figure 3: SDR-HDR mapping characteristics of typical scenes.
Figure 4: Residual clustering in the energy-structure space and physical attribution.
Figure 5: Comparison of SDR and HDR color distributions in ICtCp space.
Metric
Value
Meaning
Hue Stability (Mean ∣Δh∣ )
2.38 ∘
Overall hue shift level
Hue Outliers (P95 ∣Δh∣ )
6.52 ∘
Hue maintenance capability under extreme conditions
Chroma Correlation
0.9853
Degree of chroma correlation between SDR and HDR
Saturation Enhancement Ratio (20–100 cd/m 2 )
66.9%
Proportion of pixels where HDR saturation exceeds SDR in the 20–100 cd/m 2 region
Table 3: StEM2 color statistical metrics (ICtCp space).
Luminance Bin (HDR)
Mean Hue Diff ( ∣Δh∣ )
P95 Hue Diff
Mean Chroma Change ( ΔC )
Saturation Enhancement Ratio
Pixel Ratio
<20 cd/m 2
2.5 ∘
5.8 ∘
−0.039
30.8%
85.8%
20–100 cd/m 2
3.9 ∘
9.2 ∘
+0.003
66.9%
11.8%
>100 cd/m 2
5.7 ∘
12.5 ∘
−0.008
34.4%
2.4%
Table 4: StEM2 color metrics by luminance bin (ICtCp space).
Figure 6: Pixel-level decision maps in representative scenes. Green indicates EXR-closer recovery under the operational rule; red indicates content-adaptive adjustment.
Scene
EXR-Closer Recovery (Green)
Content-Adaptive Adjustment (Red)
Day Car Interior
80.0%
20.0%
Night Car Interior
76.7%
23.3%
Cave
77.7%
22.3%
Desert
97.9%
2.1%
Hybrid VFX
84.4%
15.6%
Night Interior
79.6%
20.4%
Table 5: StEM2 EXR-closer recovery vs. content-adaptive adjustment ratios.
Scene
Corr. (EXR–HDR)
Corr. (HDR–SDR)
Notes
Desert
0.72
0.993
High dynamic range, high-SNR scene; SDR mainly constrained by luminance container compression