Efficient scalable coding of stereophonic audio by conditional quantization and estimation-theoretic prediction

The standard scalable coding of stereophonic audio suffers from significant performance loss because of (1) poor prediction gain at the enhancement-layer and (2) direct requantization of the reconstruction error, which is suboptimal for the noise-mask ratio (NMR) criterion. To mitigate such performance loss, this paper proposes an integrated approach which employs two complementary techniques, namely, the estimation theoretic (ET) predictor and the conditional enhancement-layer quantizer (CELQ). The ET predictor has been shown to combine information from various sources for efficient enhancement-layer prediction, while CELQ efficiently handles scalable quantization to minimize NMR. We demonstrate that the proposed combined approach can achieve major performance gains in terms of bit rate reduction and reconstruction quality enhancement. For example, the proposed 2×16 kbit/s two layer coder achieves considerably improved reconstruction quality compared to that of the conventional 4×16 kbit/s four layer coder, despite expending only 50% of the standard scalable coder bit rate.