Numerical Evaluation of Sparse Residual Correction for Joint Luminance and Chromaticity Calibration in Mini-LED Displays

RGB channel response differences and model prediction bias limit joint grayscale luminance and chromaticity calibration in passive-matrix (PM) Mini-LED displays. A method combining local response modeling and output residual correction is therefore proposed. A forward model constructed from single-channel and equal-RGB-code responses is used to optimize a baseline compensation mapping allowing separate RGB drive-code adjustments. At five baseline compensation operating points, residuals are calculated as the differences between simulated outputs and model predictions evaluated at the same RGB triplet. These residuals are interpolated along the gray-level axis to update luminance and chromaticity predictions before reoptimizing the mapping. Numerical evaluation based on measured base-response data shows that, over 250 gray levels excluded from residual sampling in the original scenario, joint correction reduces the mean full-scale-normalized absolute luminance error from 0.2885% to 0.1028% and the mean chromaticity deviation Δu′v′ from 0.002823 to 0.001126 relative to the baseline mapping. Compared with residual correction using existing equal-RGB-code responses, these metrics decrease by a further 20.1% and 8.1%, respectively. Both advantages persist across 30 numerical recalibration runs in the original scenario. However, in scenario S1 with stronger candidate-code-dependent deviations, joint correction produces a higher mean luminance error than the baseline, while retaining a lower mean chromaticity deviation. The results indicate that sparse residual feedback can improve calibration at unsampled gray levels when sampled residuals adequately represent prediction bias within the candidate domain. Independent measurements at non-equal RGB triplets are still needed to validate physical calibration performance.