SHVC All Intra 공간 스케일러빌리티를 위한 효율적인 모드 결정 알고리즘

Abstract

This paper proposes an efficient mode decision method for All Intra (AI) spatial scalability in SHVC. The proposed method can significantly reduce the encoding time by effectively reducing the number of candidate modes in the rough mode decision (RMD) step as well as the number of candidate modes in the rate-distortion optimization (RDO) step. In the RMD step, a new absolute difference of average (ADA) measure is proposed that can dramatically reduce the number of intra-modes to be examined. The proposed method uses different threshold values depending both on the direction and the CU sizes for the best tradeoff between the performance and the encoding complexity. In the RDO step, the proposed method makes use of three kinds of candidate modes, which are base-layer (BL) correlated modes, spatially correlated modes, and the candidate modes from the RMD step. By efficiently combining different kinds of candidate modes to be examined, the proposed method not only reduces the encoding complexity significantly but also shows better performance compared to other mode decision methods.