To address the fine-grain scalable audio compression issue, a novel combined significance tree technique is proposed for high compression efficiency. The core idea is to dynamically adopt a set of locally optimal significance trees, instead of following the common approach of using a single type of tree. Two different encoding strategies are proposed: the spectral coefficients can be encoded either in a threshold-by-threshold manner or in a segment-by-segment manner. The former yields rate and fidelity scalability, and the latter additionally yields bandwidth scalability. Experimental results show that our proposed scheme significantly outperforms the existing schemes using single-type trees and performs comparably with the MPEG AAC coder while achieving fine-grain scalability.
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