This paper proposes, inspired by local binary pat- tern (LBP) and its variants, a novel local texture operator for texture modelisation and classification, referred to as Multi-scale Ternary and Septenary Pattern (MTSP). MTSP is a histogram- based feature representation designed based on two single-scale STP and SSP encoders (single-scale ternary and septenary pat- terns, respectively). STP and SSP are built using a new set theory- based pattern encoding technique that combines the concpet of both LTP’s and LQP’s texture descriptors. The main idea behind STP and SSP is to calculate several virtual pixels based on different local and global image statistics and to progressively encode both local and non-local pixel interactions by analyzing the differential excitation and direction information based on the relationships between pixels sampled in different locations. Then, the obtained histograms of SSP and TSP methods are concatenated to form the final MTSP feature vector. Experiments have shown that MTSP has better performance stability across nine texture datasets than many recent state-of-the-art texture approaches.