In this paper, we propose a framework for texture classification that improves upon the traditional local directional ternary pattern (LDTP) by integrating a performance-enhanced directional mask (PEDM). This innovative approach addresses the limitations of existing texture analysis techniques, such as time-consuming, computational inefficiency, and limited robustness, by using optimized directional masks and Gaussian second-derivative filters to achieve higher noise resilience and efficiency. PEDM captures both structural and directional texture details, enabling robust encoding of local spatial patterns with low time complexity. The resulting proposed RTC-LDTP algorithm generates distinct upper and lower pattern codes, which are combined into a discriminative feature vector for classification. Employing a Support Vector Machine (SVM) classifier, the proposed method demonstrates significant performance improvements across multiple datasets, including KTH-TIPS, KTH-TIPS2-b and CUReT achieving competitive accuracy and reducing computational overhead. This study establishes PEDM as a robust solution for real-world texture classification, setting new benchmarks in accuracy and efficiency.