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Gaussian Source Coding using a Simple Switched Quantization Algorithm and Variable Length CodewordsPERIC, Z. , PETKOVIC, G. , DENIC, B. , STANIMIROVIC, A. , DESPOTOVIC, V. , STOIMENOV, L.
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Gaussian distribution, quantization, source coding, signal processing algorithms, signal to noise ratio
source(8), coding(8), speech(7), signal(7), gaussian(7), quantization(6), scalar(5), quantizers(5), logarithmic(5), optimal(4)
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About this article
Date of Publication: 2020-11-30
Volume 20, Issue 4, Year 2020, On page(s): 11 - 18
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2020.04002
Web of Science Accession Number: 000594393400002
SCOPUS ID: 85098140347
This paper introduces an algorithm based on switched scalar quantization utilizing a novel -law quantization model (optimized in terms of minimal distortion) and variable length codewords, for high-quality encoding of the signals modeled by Gaussian distribution. The implemented -law quantizer represents an improvement of the standard -law quantizer in terms of bit rate, at the same time providing the equal signal quality. The main concept of the algorithm is to divide the range of the input signal variances into a certain number of sub-ranges, and to design the optimal quantizer for each sub-range. The signal is processed frame-by-frame, and for each frame the best performing quantizer is chosen, where the estimated frame variance is used as the switching criterion. Theoretical results indicate that the proposed algorithm achieves performance comparable to the standard -law quantizer, enabling the compression of about 0.5 bit/sample. The simulation results are provided to confirm the correctness of the proposed model.
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 Robust 2-bit Quantization of Weights in Neural Network Modeled by Laplacian Distribution, PERIC, Z., DENIC, B., DINCIC, M., NIKOLIC, J., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 3, Volume 21, 2021.
Digital Object Identifier: 10.4316/AECE.2021.03001 [CrossRef] [Full text]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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