The main aim of the paper is to provide effective and accurate solutions for the calculation of the support region of the μ-law logarithmic companding quantizers. A new solution for the starting ...point of iterative methods will be proposed, that provides very accurate value of the support region (being the main parameter needed for the design of the quantizer) only after one iteration of the iterative method. Based on this new starting point, an accurate closed-form approximate expression for the calculation of the support region will be derived, as one of the main contributions of the paper. To significantly simplify implementation of the μ-law companding quantizer, piecewise linearization is performed. A new linearization method is presented, based on the optimization of the last segments. Derivation of an accurate closed-form formula for the support region of the linearized quantizer is done, as an important contribution. The obtained linearized μ-law companding quantizer is very simple to design (due to closed-form formulas) and to implement (due to linearization), providing at the same time very high performance (due to optimization of the last segments). Due to these and other advantages (robustness, adjustability to the statistical distribution of the input signal), the proposed quantizer can be used in many topical applications, such as in receivers of 5G wireless systems or in neural networks for quantization of weights and activations. The paper provides an application of the designed quantizers for quantization of weights of a neural network, showing significant decreasing of the bit-rate compared to the standard full-precision representation (from 32 bits to just 5 bits), with the same prediction accuracy of the network.
•A very accurate closed-form expression for the support region of the µ-law logarithmic quantizer is provided.•Implementation complexity of the µ-law logarithmic quantizer is drastically reduced by optimized linearization.•Weights of neural networks can be drastically compressed if they are quantized with the proposed quantizer.
U ovoj disertaciji razmatra se projektovanje kvantizera, uređajakoji se u okviru telekomunikacionih sistema koriste za kodovanjesignala. Glavni cilj ove disertacije je konstrukcija ...logaritamskihkvantizera za kodovanje govornog signala koji će obezbediti visokkvalitet rekonstruisanog signala na prijemnoj strani. Upotrebomtransformacionih i tehnika adaptacije poboljšava se kvalitetkodovanja u odnosu na klasične telekomunikacione sistemepredložene od strane Međunarodne Telekomunikacione Unije.Kvantizacija, jedan od najznačajnijih procesa pri obradisignala, predstavlja diskretizaciju po amplitudi kao osnovni korak upostupku digitalizacije analognih signala. Karakteristika održanjapribližno konstantnog kvaliteta kodovanja u širokom opseguvarijansi naziva se robustnost, te je pogodno koristiti logaritamskekvantizere koji zadovoljavaju ovu karakteristiku. Adaptacijomlogaritamskih kvantizera povećava se kvalitet kodovanja jeradaptivni kvantizeri prate promenu snage signala i prilagođavajukvantizacione nivoe čime obezbeđuju konstantno visok kvalitet uširokom opsegu varijansi.Primenom metoda transformacionog kodovanja dodatno rastekvalitet kodovanja govornog signala. Transformaciono kodovanjepodrazumeva udruživanje uzastopnih odmeraka signala i primenuodređene transformacije nad njima čime se vrši preraspodelaenergije (informacije), što predstavlja dekorelaciju signala. U ovoj disertaciji za projektovanje kodnih šema iskorišćene su neke odnajpoznatijih transformacija као što su Hadamarova, diskretnakosinusna i diskretnawavelettransformacija.Dovođenjem diskretnog signala na ulaz sistema za kodovanjedodatno se povećava kompresija jer je diskretni signal amplitudskiograničen i nema distorzije prekoračenja što i na nižim bitskimbrzinama obezbeđuje visok kvalitet kodovanja. Iz ovog razlogapredložene su šeme za kodovanje diskretnog ulaznog signala.Doprinos predloženih rešenja u okviru ove disertacije jasno sepokazuje kombinacijom navedenih tehnika kvantizacije itransformacionog kodovanja, jer se postiže visok kvalitetrekonstrukcije govornog signala na prijemu i na nižim bitskimbrzinama.Predložena tema je aktuelna sa naučnog stanovišta jer sarazvojem komunikaciono-informacionih tehnologija itelekomunikacionih sistema primena predloženih rešenjatelekomunikacionih sistema u prenosu govornog signala postaje svezastupljenija. Rezultati izloženi u ovoj disertaciji mogu imati ipraktičnu primenu.
BTC (Block Truncating Coding) is a well-known image coding algorithm. This paper presents the application of BTC algorithm on the speech signal. BTC splits the input signal into blocks which are ...processed separately. The original algorithm is modified for application in speech signal coding and the quantizer design is presented.
The main aim of the paper is to provide effective and accurate solutions for the calculation of the support region of the logarithmic companding quantizers. A new solution for the starting point of ...iterative methods will be proposed, that provide very accurate value of the support region only after one iteration of the iterative method. Based on this new starting point, an accurate closed-form approximate expression for the calculation of the support region will be derived, as a main contribution of the paper. Due to their numerous advantages (robustness, adjustability to the statistical distribution of the input signal), logarithmic quantizers are considered to be used in many topical applications, such as in receivers of 5G wireless systems or in neural networks for quantization of weights and activations.
