Noise analysis of modulated quantizer based on oversampled signals

In this paper, a noise analysis of a modulated quantizer is performed. If input signals are oversampled, then the quantization error could be reduced by modulating both the input and the output of the quantizer. The working principle is based on the fact that convolutions of bandpass signals would spread wider in the frequency spectrum than that of lowpass signals. Hence, by filtering the high frequency components, the signal-to-noise ratio (SNR) could be increased. Numerical simulation results show that the modulated quantization scheme could achieve an average of 13.0960dB to 21.4700dB improvements on SNR over the conventional scheme, depends on the types of bandlimited input signals

Combining nonlinear multiresolution system and vector quantization for still image compression

It is popular to use multiresolution systems for image coding and compression. However, general-purpose techniques such as filter banks and wavelets are linear. While these systems are rigorous, nonlinear features in the signals cannot be utilized in a single entity for compression. Linear filters are known to blur the edges. Thus, the low-resolution images are typically blurred, carrying little information. We propose and demonstrate that edge- preserving filters such as median filters can be used in generating a multiresolution system using the Laplacian pyramid. The signals in the detail images are small and localized in the edge areas. Principal component vector quantization (PCVQ) is used to encode the detail images. PCVQ is a tree-structured VQ which allows fast codebook design and encoding/decoding. In encoding, the quantization error at each level is fed back through the pyramid to the previous level so that ultimately all the error is confined to the first level. With simple coding methods, we demonstrate that images with PSNR 33 dB can be obtained at 0.66 bpp without the use of entropy coding. When the rate is decreased to 0.25 bpp, the PSNR of 30 dB can still be achieved. Combined with an earlier result, our work demonstrate that nonlinear filters can be used for multiresolution systems and image coding

Vector quantization

During the past ten years Vector Quantization (VQ) has developed from a theoretical possibility promised by Shannon's source coding theorems into a powerful and competitive technique for speech and image coding and compression at medium to low bit rates. In this survey, the basic ideas behind the design of vector quantizers are sketched and some comments made on the state-of-the-art and current research efforts

Improving the robustness of CELP-like speech decoders using late-arrival packets information : application to G.729 standard in VoIP

L'utilisation de la voix sur Internet est une nouvelle tendance dans Ie secteur des télécommunications et de la réseautique. La paquetisation des données et de la voix est réalisée en utilisant Ie protocole Internet (IP). Plusieurs codecs existent pour convertir la voix codée en paquets. La voix codée est paquetisée et transmise sur Internet. À la réception, certains paquets sont soit perdus, endommages ou arrivent en retard. Ceci est cause par des contraintes telles que Ie délai («jitter»), la congestion et les erreurs de réseau. Ces contraintes dégradent la qualité de la voix. Puisque la transmission de la voix est en temps réel, Ie récepteur ne peut pas demander la retransmission de paquets perdus ou endommages car ceci va causer plus de délai. Au lieu de cela, des méthodes de récupération des paquets perdus (« concealment ») s'appliquent soit à l'émetteur soit au récepteur pour remplacer les paquets perdus ou endommages. Ce projet vise à implémenter une méthode innovatrice pour améliorer Ie temps de convergence suite a la perte de paquets au récepteur d'une application de Voix sur IP. La méthode a déjà été intégrée dans un codeur large-bande (AMR-WB) et a significativement amélioré la qualité de la voix en présence de <<jitter » dans Ie temps d'arrivée des trames au décodeur. Dans ce projet, la même méthode sera intégrée dans un codeur a bande étroite (ITU-T G.729) qui est largement utilise dans les applications de voix sur IP. Le codeur ITU-T G.729 défini des standards pour coder et décoder la voix a 8 kb/s en utilisant 1'algorithme CS-CELP (Conjugate Stmcture Algebraic Code-Excited Linear Prediction).Abstract: Voice over Internet applications is the new trend in telecommunications and networking industry today. Packetizing data/voice is done using the Internet protocol (IP). Various codecs exist to convert the raw voice data into packets. The coded and packetized speech is transmitted over the Internet. At the receiving end some packets are either lost, damaged or arrive late. This is due to constraints such as network delay (fitter), network congestion and network errors. These constraints degrade the quality of speech. Since voice transmission is in real-time, the receiver can not request the retransmission of lost or damaged packets as this will cause more delay. Instead, concealment methods are applied either at the transmitter side (coder-based) or at the receiver side (decoder-based) to replace these lost or late-arrival packets. This work attempts to implement a novel method for improving the recovery time of concealed speech The method has already been integrated in a wideband speech coder (AMR-WB) and significantly improved the quality of speech in the presence of jitter in the arrival time of speech frames at the decoder. In this work, the same method will be integrated in a narrowband speech coder (ITU-T G.729) that is widely used in VoIP applications. The ITUT G.729 coder defines the standards for coding and decoding speech at 8 kb/s using Conjugate Structure Algebraic Code-Excited Linear Prediction (CS-CELP) Algorithm

A practical postprocessing technique for real-time block-based coding system

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Period Information Deviation on the Segmental Sinusoidal Model

Speech signal can be modeled by sinusoidal model. On the sinusoidal model, there are many kinds for representing the signal. One of model is Segmental Sinusoidal model. The segmental sinusoidal model is an approximation method based on sinusoidal model for speech signal, especially for periodic detection. The periodic signal can be decomposed by infinite sinusoidal signal with combination of amplitude, frequency and phase. After the signal is decomposed, parameter will be quantized. The proposed quantization method in this paper is sampling signal on big part between minimum and maximum part over observation block. Some parameters of speech signal are detected. The useful parameters are peaks and period between consecutive peaks. Period information obtained from this quantization tends to different than the original, In this paper, we show the experimental results that there are many differences between period information on encoder side with the decoder side. It caused by quantization error on period information and quantization error on the codebook design. Effect of differences is degradation of signal quality, especially on frequency signal accuracy. On this paper, deviation of the reconstructed signal from original signal will be evaluated. Deviation from the original signals means that some error occur on period quantization

Period Information Deviation on the Segmental Sinusoidal Model

Speech signal can be modeled by sinusoidal model. On the sinusoidal model, there are many kinds for representing the signal. One of model is Segmental Sinusoidal model. The segmental sinusoidal model is an approximation method based on sinusoidal model for speech signal, especially for periodic detection. The periodic signal can be decomposed by infinite sinusoidal signal with combination of amplitude, frequency and phase. After the signal is decomposed, parameter will be quantized. The proposed quantization method in this paper is sampling signal on big part between minimum and maximum part over observation block. Some parameters of speech signal are detected. The useful parameters are peaks and period between consecutive peaks. Period information obtained from this quantization tends to different than the original, In this paper, we show the experimental results that there are many differences between period information on encoder side with the decoder side. It caused by quantization error on period information and quantization error on the codebook design. Effect of differences is degradation of signal quality, especially on frequency signal accuracy. On this paper, deviation of the reconstructed signal from original signal will be evaluated. Deviation from the original signals means that some error occur on period quantizatio