For the conventional block floating-point quantizer (BFPQ), usually a large block size causes performance degradation and thus small block sizes are preferred, especially when non-uniformly distributed signals are processed. A tunable BFPQ with fractional exponent is proposed in this paper to deal with the problem. We first examine the root cause of degradation through analytic equations and then propose to tune the thresholds for deriving the exponent and fractional exponent of the block so as to strike a good balance between the quantization error and saturation error. An optimal tuning value depending on the block size and mantissa word-length can be obtained. Thus, the tunable BFPQ can achieve better output signal-to-quantization-noise ratio (SQNR) in a wide dynamic range. The analytic equation for the output SQNR of the proposed BFPQ is derived to verify the simulated results. Only one extra multiplication is required for each block to implement the tunable BFPQ. Finally, we show the obvious SQNR improvements compared to the conventional scheme for various settings of block sizes and mantissa word-lengths.
A high-speed and high-resolution block adaptive quantizer is designed and implemented for current synthetic aperture radar imaging systems. To solve the problem of exponential growth in complexity due to the requirements of large BAQ output wordlengths, input scaling and hybrid comparison architectures are proposed. Significant saving in the threshold memory size is achieved and a good balance of path delay and arithmetic complexity is attained by two techniques. The proposed design supports 291MHz and 12-bit ADC with output wordlengths of 2, 3, 4, 6 bits.
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.
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