Fast Mode Decision for Coarse Granular Scalability via Switched Candidate Mode Set

This paper presents an improved fast mode decision algorithm for coarse grain scalability (CGS). The modified fast mode decision algorithm extends previous work to provide better encoder complexity reduction with insignificant degradation in picture quality. The candidate mode set is adaptive to the quantization parameter difference between a layer and its "base layer". Furthermore, the proposed scheme fully utilizes the statistics of mode transition between a layer and its "base layer" when the quantization parameter difference between these two layers is small. Simulation results demonstrate that the proposed scheme provides up to 64% of time saving compared with the original SVC encoder.