Generalized interpolation for motion compensated prediction
Fractional sample interpolation with FIR filters is commonly used for motion compensated prediction (MCP). The FIR filtering can be viewed as a signal decomposition using restricted basis functions. The concept of generalized interpolation provides a greater degree of freedom for selecting basis functions. We implemented generalized interpolation using a combination of short IIR and FIR filters. An efficient multiplication-free design of the algorithm that is suited for hardware implementation is shown. Compared to a 6-tap FIR interpolation filter, average rate savings of 3.1% are observed. A detailed analysis of the complexity and memory bandwidth cycles compared to existing interpolation techniques for MCP is provided.