投影残差矢量量化和映射残差池化

Ryan P. Thomas, T. Moon
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引用次数: 1

摘要

指出了残差矢量量化(RVQ)存在的两个潜在问题:树纠缠和量化器的非投射性。提出了使用边界归一化映射将某一阶段的所有量化残差汇集到相同形状的区域中,从而减少或消除纠缠。同时,提出了一种重构码本来消除非投影性。在随机数据和图像数据上给出了结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Projective residual vector quantization and mapped residual pooling
This paper points out two potential problems with residual vector quantization (RVQ): tree entanglement and non-projectiveness of the quantizer. The use of a boundary normalization mapping is proposed to pool all quantization residuals at a stage into identically-shaped regions, reducing or eliminating entanglement. Also, a reconstruction codebook is proposed to eliminate the non-projectiveness is proposed. Results are presented on both random and image data.
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