This paper proposes a theoretical framework to address the reduced biquaternion equality-constrained total least squares (RBTLSE) problem. The objective is to find an approximate solution to the system \(AX \approx B\) , subject to linear constraints \(CX = D\) , while explicitly accounting for errors in both the coefficient matrix A and the observation matrix B. We establish conditions under which real, complex, and reduced biquaternion solutions exist and develop corresponding solution methods using real and complex representations of reduced biquaternion matrices. To assess the sensitivity of the solutions to data perturbations, we establish upper bounds for the relative forward errors. These results ensure the computational reliability of the proposed framework. Extensive numerical experiments validate the theoretical findings and demonstrate that the RBTLSE approach significantly outperforms the conventional reduced biquaternion equality-constrained least squares (RBLSE) method, particularly in noisy environments affecting both sides of the system. Furthermore, the proposed framework is applied to color image encryption and decryption to demonstrate its practical effectiveness.
