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An Adaptive Technique for Accuracy Enhancement of Vector Quantizers in Nonorthogonal Domains

机译:一种非正交域矢量量化器精度增强的自适应技术

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摘要

Recently, novel vector quantization techniques in multiple nonorthogonal domains for both waveform and model-based signal characterization that give an improved qualitative and quantitative signal coding performance as compared to vector quantization in single domain have been reported. In these techniques, vectors are formed either directly from the signal waveform or from the model parameters extracted from the signal. Then, the vectors are represented in multiple nonorthogonal domains. The encoder chooses the domain that best represents the vector according to a predetermined criterion. An iterative codebook enhancement algorithm, applicable to both waveform and model-based vector quantization in nonorthogonal domains is developed and presented in this paper. In this algorithm, each set of codebooks in a given domain is retrained by the vectors that were best represented by that particular set of codebooks in the most recent iteration. The algorithm is applied successfully and extensive simulation results yield considerable performance enhancement of the vector quantization in nonorthogonal domains, for a given bit rate. Sample results are provided which demonstrate the improved performance.
机译:最近,已经报道了在多个非正交域中用于波形和基于模型的信号表征的新颖矢量量化技术,与单域中的矢量量化相比,该技术提供了改进的定性和定量信号编码性能。在这些技术中,直接从信号波形或从信号中提取的模型参数形成矢量。然后,向量在多个非正交域中表示。编码器根据预定标准选择最能代表矢量的域。本文提出并提出了一种迭代码本增强算法,该算法适用于非正交域中的波形和基于模型的矢量量化。在该算法中,给定域中的每组密码本都由在最近的迭代中由该特定密码本集最佳表示的向量进行重新训练。对于给定的比特率,该算法已成功应用,大量的仿真结果显着提高了非正交域中矢量量化的性能。提供的示例结果证明了性能的提高。

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