This paper presents a systematical approach to evaluate a system from both perspectives of algorithmic performance and complexity that could be considered as potential architecture cost. The complexity metrics include number of operations, data storage requirement, data transfer rate, and numbers of storage accessing; and these factors have the merits that are transparent to either algorithm or architecture. A case study of the coding tool, Backward View Synthesis Prediction (BVSP) in 3D-HEVC, is provided to demonstrate the evidence of the proposed approach. BVSP provides an effective BD-rate reduction through synthesizing a virtual view from depth information in removing inter-view redundancy. However, the coding performance and the complexity of BVSP would be distinct at various processing granularities. This paper tradeoffs between coding performance and algorithmic complexity via exploring various processing granularities; furthermore, an adaptive strategy that determines the processing granularity according to global depth distribution and local depth variation is also proposed to determine suitable processing granularity. This method decreases complexity but remains comparative coding performance. Consequently, in comparison with HTM-10.0r1, the experimental result shows no BD-rate increasing on average and the complexity of proposed method shows that the data transfer rate could be reduced 6.49% and 11.90% at average and best scenarios; in addition, the number of storage accessing also could be reduced 31.03% and 87.27% at average and best scenarios.