A New Method for Camera Auto White Balance for Portrait
Abstract
:1. Introduction
2. Materials and Methods
2.1. Theoretical Basis of SCR-AWB Algorithm
2.1.1. Spectral Estimation of Unknown Light Sources
2.1.2. From Predicted Spectra to CCT
2.1.3. From Predicted Spectra to Gain
2.2. Parameter Acquisition
2.2.1. Prior Information Acquisition
2.2.2. Image Information Acquisition
3. Experiments
3.1. Experiment 1: Color Chart White Point and Neutral Gray Evaluation Under Different CCT Artificial Light Sources
3.2. Experiment 2: Skin Color Reproduction Under Different CCT Artificial Light Sources
- 1.
- Comparison of SCR-AWB Algorithm Predicted CCT with Calibrated Laboratory CCT: As outlined in the methodology, the SCR-AWB algorithm predicts the SPD, which is then converted to CCT. White balance accuracy is assessed by calculating the difference, , between the algorithm-predicted CCT and the actual calibrated CCT, measured using a spectroradiometer (CS-2000, KONICA MINOLTA, Inc., Osaka, Japan). A smaller indicates a closer match between the predicted ambient light and actual lighting conditions, thereby enhancing white balance performance.
- 2.
- Evaluation of AWB Results on sRGB Output for DCI-P3 Display: The SCR-AWB algorithm outputs both CCT and gain values for the R and B channels. These gain values are applied in the image processing pipeline to adjust white balance, resulting in the final output in sRGB format (JPEG). Other AWB algorithms used for comparison also modify only the R and B channel gains, ensuring that BLC, CCM, Gamma correction, and other processing steps are kept consistent across all algorithms for an accurate evaluation of white balance adjustments.
4. Results
5. Discussion and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
MDPI | Multidisciplinary Digital Publishing Institute |
DOAJ | Directory of open access journals |
TLA | Three letter acronym |
LD | Linear dichroism |
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Algorithm | Mean | Median | Best 25% | Worst 25% | Maximum |
---|---|---|---|---|---|
GW ** | 3.20 ± 1.10 | 3.14 | 2.58 | 3.98 | 4.19 |
Max-RGB | 4.79 ± 4.07 | 3.75 | 2.70 | 6.14 | 9.82 |
SoG * | 2.41 ± 1.03 | 2.59 | 2.17 | 3.03 | 3.16 |
GGW * | 1.96 ± 0.50 | 1.92 | 1.70 | 2.00 | 2.60 |
GE1 ** | 10.45 ± 2.92 | 10.58 | 9.27 | 12.04 | 13.01 |
GE2 ** | 10.86 ± 3.15 | 10.87 | 8.95 | 11.36 | 14.99 |
Bianco’s [20] | 3.45 ± 3.99 | 2.31 | 1.73 | 3.57 | 8.90 |
SCR-AWB | 0.88 ± 0.55 | 0.87 | 0.50 | 0.98 | 1.57 |
Algorithm | Recovery Angle Error | Algorithm | Reproduction Angle Error | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Mean | Median | Best 25% | Worst 25% | Maximum | Mean | Median | Best 25% | Worst 25% | Maximum | ||
GW ** | 2.76 ± 0.65 | 2.39 | 1.66 | 3.18 | 8.15 | GW | 1.89 ± 0.89 | 1.73 | 1.30 | 2.39 | 2.86 |
Max-RGB ** | 3.84 ± 1.48 | 2.65 | 0.92 | 5.40 | 11.77 | Max-RGB | 3.81 ± 4.95 | 2.52 | 0.76 | 5.40 | 9.98 |
SoG ** | 1.97 ± 0.42 | 1.76 | 1.39 | 2.54 | 4.63 | SoG * | 1.62 ± 0.85 | 1.43 | 1.35 | 1.89 | 2.63 |
GGW ** | 1.98 ± 0.50 | 1.38 | 1.10 | 2.53 | 5.68 | GGW | 1.85 ± 0.50 | 1.86 | 1.27 | 2.48 | 2.48 |
GE1 ** | 11.07 ± 1.61 | 10.49 | 8.38 | 13.66 | 19.08 | GE1 ** | 9.73 ± 4.62 | 8.52 | 6.61 | 13.66 | 13.71 |
GE2 ** | 10.94 ± 1.68 | 10.51 | 7.13 | 12.44 | 18.05 | GE2 * | 10.21 ± 5.80 | 10.51 | 6.18 | 11.41 | 17.24 |
Bianco’s [20] ** | 1.73 ± 0.40 | 1.58 | 1.01 | 2.31 | 4.36 | Bianco’s [20] | 1.93 ± 1.20 | 1.87 | 1.84 | 1.91 | 3.39 |
Afifi’s [35] ** | 8.13 ± 2.99 | 5.59 | 2.47 | 13.66 | 22.71 | Afifi’s [35] | 8.84 ± 10.68 | 5.19 | 2.47 | 15.23 | 20.52 |
SCR-AWB | 1.16 ± 0.23 | 1.08 | 0.83 | 1.29 | 3.01 | SCR-AWB | 0.91 ± 0.43 | 0.95 | 0.63 | 1.12 | 1.35 |
Scene | Mean | Median | Best 25% | Worst 25% | Maximum |
---|---|---|---|---|---|
2300 K | 97 | 93 | 51 | 101 | 252 |
3500 K | 63 | 60 | 15 | 109 | 132 |
4000 K | 52 | 35 | 24 | 75 | 114 |
5000 K | 149 | 147 | 141 | 169 | 215 |
6000 K | 231 | 138 | 65 | 383 | 560 |
6500 K | 264 | 213 | 170 | 288 | 542 |
8000 K | 381 | 299 | 208 | 435 | 1326 |
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Zhou, S.; Xiao, K.; Li, C.; Lai, P.; Luo, H.; Sun, W. A New Method for Camera Auto White Balance for Portrait. Technologies 2025, 13, 232. https://doi.org/10.3390/technologies13060232
Zhou S, Xiao K, Li C, Lai P, Luo H, Sun W. A New Method for Camera Auto White Balance for Portrait. Technologies. 2025; 13(6):232. https://doi.org/10.3390/technologies13060232
Chicago/Turabian StyleZhou, Sicong, Kaida Xiao, Changjun Li, Peihua Lai, Hong Luo, and Wenjun Sun. 2025. "A New Method for Camera Auto White Balance for Portrait" Technologies 13, no. 6: 232. https://doi.org/10.3390/technologies13060232
APA StyleZhou, S., Xiao, K., Li, C., Lai, P., Luo, H., & Sun, W. (2025). A New Method for Camera Auto White Balance for Portrait. Technologies, 13(6), 232. https://doi.org/10.3390/technologies13060232