Low Complex Forward Adaptive Loss Compression Algorithm and Its Application in Speech Coding

Abstract

This paper proposes a low complex forward adaptive loss compression algorithm that works on the frame by frame basis. Particularly, the algorithm we propose performs frame by frame analysis of the input speech signal, estimates and quantizes the gain within the frames in order to enable the quantization by the forward adaptive piecewise linear optimal compandor. In comparison to the solution designed according to the G.711 standard, our algorithm provides not only higher level of the average signal to quantization noise ratio, but also performs a reduction of the PCM bit rate for about 1 bits/sample. Moreover, the algorithm we propose completely satisfies the G.712 standard, since it provides overreaching the curve defined by the G.712 standard in the whole of variance range. Accordingly, we can reasonably believe that our algorithm will find its practical implementation in the high quality coding of signals, represented with less than 8 bits/sample, which as well as speech signals follow Laplacian distribution and have the time varying variances. K e y w o r d s: forward adaptive technique, loss compression algorithm, piecewise linear optimal compandor

Design of novel piecewise uniform scalar quantizer for Gaussian memoryless source

“…This contributes to the SQNR increase. In addition, the model we propose is of smaller complexity than the quantizer model described by Nikolic et al [2011] since it is consisted of only two uniform quantizers. By comparing the proposed quantizer model with the uniform quantizer model it can be concluded that with the proposed quantizer model, a great gain in SQNR is achieved with regard to the uniform quantizer model [Thompson et al, 2007], where the complexity of the proposed quantizer model with regard to the uniform quantizer model is higher only for the complexity amount of one uniform quantizer.…”

“…It is consisted of eight uniform quantizers (segment number is L = 8) where the number of cells is constant per segments. Unlike the piecewise uniform scalar quantizer described by Nikolic et al [2011] in this paper optimization of number of cells per segment is done. This contributes to the SQNR increase.…”

“…[27] The quantizer model described by Nikolic et al [2011] also presents a simple solution to the piecewise uniform scalar quantizer achieved by linearization of the optimal compressor function. It is consisted of eight uniform quantizers (segment number is L = 8) where the number of cells is constant per segments.…”

“…[5] A comprehensive analysis of signal to quantization noise ratio (SQNR) behavior in a wide range of variances for the PUSQ designed for a Laplacian source according to the piecewise linear approximation to the optimal compressor law is reported in the work of Nikolic et al [2011]. It is emphasized that the uniform quantizer may not be realized as a compandor with a linear compressor function.…”

Improved Linearization of the Optimal Compression Function for Laplacian Source

“…The piecewise uniform scalar quantizer is analyzed in [2]. By the algorithm realization for the speech signal [2], not only that the higher quality signal than a quality defined by standard G.711 is obtained, but the bit-rate reduces for about 1bit/samples. The linearization of the optimal compressor function is done in [3,4].…”