Color difference is a crucial index used by the industry to evaluate the accuracy of color reproduction, especially in fields like display, textiles, and printing. However, in practical application, there are instances where the color difference measured by instruments exceeds tolerance limits but is visually acceptable, or where the measured color difference is within tolerance but is still visually unacceptable. This inconsistency may arise from the various components of total color difference, including lightness, hue, and chroma differences, which evoke varying color perceptions in the human eye. The challenge is to align the calculated color difference with the visual color difference perceived by humans, which remains a key technical issue.

To improve the accuracy of color quality evaluation for printed samples, two experimental groups are conducted simultaneously: one at the Beijing Institute of Graphic Communication (Exp. I) and another at Suining Kuanzhai Printing Company (Exp. II). In each experiment, 450 pairs of printed samples, representing the nine CIE-recommended color centers, are prepared, with CIELAB color differences ranging from 0.27 to 3.88 (mean is 1.91 in Exp. I) and from 0.21 to 3.92 (mean is 2.04 in Exp. II). Lightness difference (ΔL^*), hue difference (ΔHab*), and chroma difference (ΔCab*) contribute differently to the total color difference. A total of 43 observers (17 inexperienced observers in Exp. I and 26 experienced observers in Exp. II) participate in the color difference experiments. These experiments are conducted indoors with natural light from a north window, during fixed time intervals (9:00—11:00, 14:00—16:00) on a sunny day to ensure stable viewing conditions. In total, 22950 observations are collected in each experiment. The probabilities (P) for an “unacceptable color difference” are then converted into a standard normal distribution Z-score to obtain the visual color difference (ΔV). The standardized residual sum of squares (STRESS) index, recommended by CIE, is used to assess the performances of the calculated CIELAB and CIEDE2000 color differences compared to the visual color differences.

The wrong decision (W_D) results show that experienced observers outperform the inexperienced group, exhibiting higher sensitivity to color differences. Moreover, optimization of the k_L∶k_C∶k_H factors for lightness difference, chroma difference, and hue difference is applied to the original CIELAB and CIEDE2000 color difference formulas. In addition, a power function approach is used to minimize the STRESSvalue in both formulas. The results indicate that observers are more sensitive to hue differences, and therefore, it is essential to increase the weight of hue differences in the existing color difference formulas. To provide a practical method for enterprises to efficiently evaluate the color quality of printed samples, the consistency between the calculated results (e.g., ΔE and ΔH values), and visual color differences is examined. It is found that ΔE values optimized by k_H factors in CIELAB, and ΔE values optimized by k_L factors in CIEDE2000, along with ΔH thresholds, can improve consistency compared to other optimization methods.

Color quality control and evaluation are critical in the process of reproducing printed samples. In the CIEL^*a^*b^* color space, hue difference is more closely aligned with visual color difference than lightness and chroma differences. Based on this, we propose corresponding color difference and hue difference thresholds to improve the accuracy of product color quality evaluation for observers with different professional backgrounds. Specifically, the recommended ΔE_T values in CIELAB and CIEDE2000 are 1.47 and 1.20, while the recommended ΔHab,T* and ΔH_00,T values are 0.65 and 0.45 for inexperienced observers. For experienced observers, the recommended ΔE_T values in CIELAB and CIEDE2000 are 1.02 and 0.86, and the recommended ΔHab,T* and ΔH_00,T values are 0.53 and 0.34, respectively, for quality inspection processes. The methods and results presented in this study can also be extended to other fields such as lighting, display, textiles, and printing. They offer valuable guidance for color quality evaluation.