具有侧失真补偿的多重描述共轭矢量量化器

Yugang Zhou, W. Chan
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引用次数: 2

摘要

共轭矢量量化(CVQ)是一种抗信道误码的联合源信道编码方案,被用于各种流行的语音编码器,如ITU-T G.729和ISO/IEC MPEG4音频。我们提出了两种多描述CVQ (MD-CVQ)方案来对抗信道擦除错误。与传统的MD矢量量化器(MDVQs)相比,MD- cvq提供了中等计算复杂度和存储的优势。实验分别对高斯信号和语音/音频信号进行了测试。结果表明,对于低信道损失率,选择低复杂度的MD-CVQ和高信噪比(SNR)性能的MDVQ之间存在权衡。对于中高信道损失率,MD-CVQ具有较低的复杂性和与MDVQ相当的信噪比性能,是首选
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multiple Description Conjugate Vector Quantizers with Side Distortion Compensation
Conjugate vector quantizer (CVQ), a joint source channel coding scheme robust to channel bit errors, is used in various popular speech coders such as ITU-T G.729 and ISO/IEC MPEG4 audio. We propose two multiple description CVQ (MD-CVQ) schemes for combating channel erasure errors. MD-CVQ offers an advantage of moderate computational complexity and storage over conventional MD vector quantizers (MDVQs). Experiments are performed for both i.i.d. Gaussian source and speech/audio signals. Results show that for low channel loss rates, a tradeoff exists between choosing MD-CVQ for its low complexity and MDVQ for its higher signal-to-noise ratio (SNR) performance. For medium to high channel loss rates, MD-CVQ is preferred for its low complexity and comparable SNR performance to MDVQ
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