Joint Optimization of Base and Enhancement Layers in Scalable Audio Coding

Abstract

Current scalable audio coders typically optimize performance at a particular layer without regard to impact on other layers, and are thus unable to provide a performance trade-off between different layers. In the particular case of MPEG Scalable Advanced Audio Coding (S-AAC) and Scalable-to-Lossless (SLS) coding, the base-layer is optimized first followed by successive optimization of higher layers, which ensures optimality of the base-layer but results in a scalability penalty that progressively increases with the enhancement layer index. The ability to trade-off performance between different layers enables alignment to the real world requirement for audio quality commensurate with the bandwidth afforded by a user. This work provides the means to better control the performance tradeoffs, and the distribution of the scalability penalty, between the base and enhancement layers. Specifically, it proposes an efficient joint optimization algorithm that selects the encoding parameters for each layer while accounting for the rate-distortion costs in all layers. The efficacy of the technique is demonstrated in the two distinct settings of S-AAC, and SLS High Definition Advanced Audio Coding. Objective and subjective tests provide evidence for substantial gains, and represent a significant step toward bridging the gap with the non-scalable coder.