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Finite-state entropy-constrained vector quantiser for audio modified discrete cosine transform coefficients uniform quantisation

机译:音频修正有限余弦变换系数均匀量化的有限状态熵约束矢量量化器

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In this paper, an entropy-constrained vector quantiser (ECVQ) scheme with finite memory, called finite-state ECVQ (FS-ECVQ), is presented. This scheme consists of a finite-state vector quantiser (FSVQ) and multiple component ECVQs. By utilising the FSVQ, the inter-frame dependencies within source sequence can be effectively exploited and no side information needs to be transmitted. By employing the ECVQs, the total memory requirements of FS-ECVQ can be efficiently decreased while the coding performance is improved. An FS-ECVQ, designed for the modified discrete cosine transform coefficients coding, was implemented and evaluated based on the unified speech and audio coding (USAC) scheme. Results showed that the FS-ECVQ achieved reduction of the total memory requirements by 92.3%, compared with the encoder in USAC working draft 6 (WD6), and over 10%, compared with the encoder in USAC final version (FINAL), while maintaining coding performance similar to FINAL, which was about 4% better than that of WD6.
机译:本文提出了一种带有有限存储的熵约束矢量量化器(ECVQ)方案,称为有限状态ECVQ(FS-ECVQ)。该方案由一个有限状态矢量量化器(FSVQ)和多个分量ECVQ组成。通过利用FSVQ,可以有效利用源序列内的帧间相关性,并且不需要传输任何辅助信息。通过使用ECVQ,可以有效降低FS-ECVQ的总内存需求,同时提高编码性能。基于统一的语音和音频编码(USAC)方案,实现并评估了为修正的离散余弦变换系数编码而设计的FS-ECVQ。结果表明,与USAC工作草案6(WD6)中的编码器相比,FS-ECVQ减少了92.3%的总内存需求,与USAC最终版本(FINAL)中的编码器相比减少了10%以上。编码性能类似于FINAL,比WD6高约4%。

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