Many extreme-scale scientific applications generate colossal amounts of data that require an increasing number of processors for parallel visualization. Among the three well-known parallel architectures, i.e. sort-first/middle/last, sort-last, which comprises of two stages, i.e. image rendering and composition, is often preferred due to its adaptability to load balancing. We propose a generalized method, namely, Grouping More and Pairing Less (GMPL), for order-independent image composition in sort-last parallel rendering. GMPL is of two-fold novelty: i) it takes a prime factorization-based approach for processor grouping, which not only obviates the common restriction in existing methods on the total number of processors to fully utilize computing resources, but also breaks down processors to the lowest level with a minimum number of peers in each group to achieve high concurrency and save communication cost; ii) within each group, it employs an improved direct send method to narrow down each processor's pairing scope to further reduce communication overhead and increase composition efficiency. The performance superiority of GMPL over existing methods is evaluated through rigorous theoretical analysis and further verified by extensive experimental results on a high-performance visualization cluster.