Block floating point quantization (BFPQ) exploits signal statistics so that one common exponent is shared among a block of data. The output signal-to-quantization-noise ratio (SQNR) may drop due to the increase in quantization error resulted from the increment of the exponent, especially for the non-uniformly distributed input signals. The tunable BFPQ is then proposed. With the aid of the tuning parameter to enlarge the thresholds for deciding the exponent and fractional exponent, the quantization error and saturation error can be balanced and thus the output SQNR can be sustained as high as possible. Both the analytic and simulated results are provided to verify the effectiveness of the tuning parameter for Gaussian-distributed and Laplacian-distributed signals. The improvement in output SQNR compared to the conventional BFPQ is also shown. Finally, the concept is implemented to support real-time high-speed compression for high-resolution synthetic aperture radar image applications. We demonstrate that the tunable BFPQ can be accomplished with only a small overhead but brings substantial performance gain, especially for large data blocks.

中文翻译：

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用于压缩非均匀分布信号的具有分数指数的可调块浮点量化器

块浮点量化 (BFPQ) 利用信号统计，以便在数据块之间共享一个公共指数。输出信量化噪声比（SQNR）可能会因为指数的增加导致量化误差的增加而下降，特别是对于非均匀分布的输入信号。然后提出了可调 BFPQ。借助调整参数扩大决定指数和分数指数的阈值，可以平衡量化误差和饱和误差，从而尽可能保持输出SQNR。提供了分析和仿真结果以验证调谐参数对高斯分布和拉普拉斯分布信号的有效性。还显示了与传统 BFPQ 相比输出 SQNR 的改进。最后，实施该概念以支持高分辨率合成孔径雷达图像应用的实时高速压缩。我们证明了可调 BFPQ 只需很小的开销就可以实现，但会带来显着的性能提升，尤其是对于大数据块。