Frame Interpolation for Cloud-Based Mobile Video Streaming

© 2016 IEEE. Cloud-based High Definition (HD) video streaming is becoming popular day by day. On one hand, it is important for both end users and large storage servers to store their huge amount of data at different locations and servers. On the other hand, it is becoming a big challenge for network service providers to provide reliable connectivity to the network users. There have been many studies over cloud-based video streaming for Quality of Experience (QoE) for services like YouTube. Packet losses and bit errors are very common in transmission networks, which affect the user feedback over cloud-based media services. To cover up packet losses and bit errors, Error Concealment (EC) techniques are usually applied at the decoder/receiver side to estimate the lost information. This paper proposes a time-efficient and quality-oriented EC method. The proposed method considers H.265/HEVC based intra-encoded videos for the estimation of whole intra-frame loss. The main emphasis in the proposed approach is the recovery of Motion Vectors (MVs) of a lost frame in real-time. To boost-up the search process for the lost MVs, a bigger block size and searching in parallel are both considered. The simulation results clearly show that our proposed method outperforms the traditional Block Matching Algorithm (BMA) by approximately 2.5 dB and Frame Copy (FC) by up to 12 dB at a packet loss rate of 1%, 3%, and 5% with different Quantization Parameters (QPs). The computational time of the proposed approach outperforms the BMA by approximately 1788 seconds

Narrow-band Deep Filtering for Multichannel Speech Enhancement

In this paper, we address the problem of multichannel speech enhancement in the short-time Fourier transform (STFT) domain. A long short-time memory (LSTM) network takes as input a sequence of STFT coefficients associated with a frequency bin of multichannel noisy-speech signals. The network's output is the corresponding sequence of single-channel cleaned speech. We propose several clean-speech network targets, namely, the magnitude ratio mask, the complex STFT coefficients and the (smoothed) spatial filter. A prominent feature of the proposed model is that the same LSTM architecture, with identical parameters, is trained across frequency bins. The proposed method is referred to as narrow-band deep filtering. This choice stays in contrast with traditional wideband speech enhancement methods. The proposed deep filtering is able to discriminate between speech and noise by exploiting their different temporal and spatial characteristics: speech is non-stationary and spatially coherent while noise is relatively stationary and weakly correlated across channels. This is similar in spirit with unsupervised techniques, such as spectral subtraction and beamforming. We describe extensive experiments with both mixed signals (noise is added to clean speech) and real signals (live recordings). We empirically evaluate the proposed architecture variants using speech enhancement and speech recognition metrics, and we compare our results with the results obtained with several state of the art methods. In the light of these experiments we conclude that narrow-band deep filtering has very good speech enhancement and speech recognition performance, and excellent generalization capabilities in terms of speaker variability and noise type

Widely Linear State Space Filtering of Improper Complex Signals

Complex signals are the backbone of many modern applications, such as power systems, communication systems, biomedical sciences and military technologies. However, standard complex valued signal processing approaches are suited to only a subset of complex signals known as proper, and are inadequate of the generality of complex signals, as they do not fully exploit the available information. This is mainly due to the inherent blindness of the algorithms to the complete second order statistics of the signals, or due to under-modelling of the underlying system. The aim of this thesis is to provide enhanced complex valued, state space based, signal processing solutions for the generality of complex signals and systems. This is achieved based on the recent advances in the so called augmented complex statistics and widely linear modelling, which have brought to light the limitations of conventional statistical complex signal processing approaches. Exploiting these developments, we propose a class of widely linear adaptive state space estimation techniques, which provide a unified framework and enhanced performance for the generality of complex signals, compared with conventional approaches. These include the linear and nonlinear Kalman and particle filters, whereby it is shown that catering for the complete second order information and system models leads to significant performance gains. The proposed techniques are also extended to the case of cooperative distributed estimation, where nodes in a network collaborate locally to estimate signals, under a framework that caters for general complex signals, as well as the cross-correlations between observation noises, unlike earlier solutions. The analysis of the algorithms are supported by numerous case studies, including frequency estimation in three phase power systems, DIFAR sonobuoy underwater target tracking, and real-world wind modeling and prediction.Open Acces

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Development of multispectral scatter correction techniques for high resolution positron emission tomography

En tomographie d'émission par positrons (TEP), les images de très haute résolution spatiale acquises à l'aide d'une caméra basée sur de petits détecteurs discrets sont obtenues au prix d'une faible sensibilité et d'une fraction élevée d'événements diffusés dans les détecteurs. Il est proposé que ces limitations peuvent être surmontées à l'aide de l'acquisition multispectrale des événements où l'énergie des photons est enregistrée de concert avec leurs coordonnées spatiales. Cette étude porte donc sur l'établissement des outils nécessaires à l'exploitation de cette information et sur l'exploration de différentes méthodes de traitement des données multispectrales pour la TEP à haute résolution. Un modèle de dégradation spectrale des photons est proposé pour fournir un support théorique applicable aux méthodes de correction du diffusé basées sur l'énergie. Ce modèle analytique fourni une description physique complète de la propagation de photon et des processus de la détection tant dans le domaine spatial que spectral Il permet aussi de faire le lien entre certaines approches heuristiques de correction du diffusé et les hypothèses physiques sous-jacentes. En particulier, il est démontré que les méthodes de correction du diffusé à double fenêtre d'énergie et à fenêtres multiples sont toutes deux affligées de limites inhérentes qui expliquent probablement leur succès mitigé. L'acquisition multispectrale offre la possibilité de développer des méthodes de correction du diffusé dépendante de l'énergie. Deux approches ont été évaluées pour solutionner ce problème. Dans la première, un lissage spectral des données est utilisé en combinaison avec l'équilibrage multispectral de l'efficacité des détecteurs, dans une séquence de prétraitement optimale, de façon à permettre une véritable analyse dépendante de l'énergie, fenêtre par fenêtre, des données multispectrale. Dans la seconde approche, un traitement global de l'ensemble multispectral est effectué a l'aide de l'analyse des composantes principales pour à la fois réduire la variance et la dimensionalité des données. Les deux approches fournissent un ensemble de donnés adéquates pour Je traitement ultérieur du rayonnement diffusé.Abstract: PET images acquired with a high resolution scanner based on arrays of small discrete detectors are obtained at the cost of low sensitivity and increased detector scatter. It has been postulated that these limitations can be overcome by using multispectral acquisition whereby the energy information is registered together with the spatial coordinates of detected events. This work is an investigation of multispectral data processing methods for high resolution PET. A photon spectral degradation model is proposed to provide theoretical support for energy-based scatter correction methods. This analytical model supplies a complete physical description of the photon propagation and detection processes in both the spatial and spectral domain. It also helps to bridge the gap between a number of heuristic scatter correction approaches and the underlying physical assumptions. In particular, it is shown that such methods as the dual energy window and multispectral frame-by-frame scatter correction techniques have intrinsic deficiencies which may be responsible for their limited success. The potential of multispectral acquisition for developing energy-dependent scatter correction methods is severely impeded by stochastic fluctuations. Two approaches were investigated to overcome this drawback. In the first one, spectral smoothing is attempted in combination with multispectral normalization of detector efficiency and optimal data pre-processing sequence in order to allow truly energy-dependent data processing on a frame-by-frame basis. In the second approach, a global analysis of the multispectral data set is performed by the principal component analysis for reducing both the variance and dimensionality of the multispectral data. Both approaches provide improved data for further processing. The multispectral frame-by-frame convolution scatter correction protocol is shown to yield inferior performance to that of the convolution scatter correction in one broad window. It is concluded that the approximations made in each energy frame to implement the frame-by-frame approach accumulates errors in the final result. Consequently, the spectral smoothing technique and the implementation of the degradation model in the multiple window approach will have to be revisited to overcome this deficiency. A data processing protocol which combines the use of both spatial and spectral information into one scatter correction method is proposed to exploit multispectral data optimally. The method consists of two consecutive steps: first, optimal noise and data dimensionality reduction, as well as partial suppression of scatter, is achieved by performing the global analysis of the multispectral data set; second, a spatial scatter correction technique, the object scatter subtraction and detector scatter restoration algorithm in this study, is used to correct for the residual scatter contribution in the output of the first step. The relevance of such a correction scheme for multispectral data is demonstrated by its superior performance as compared to conventional spatial scatter correction methods. This global scatter correction approach is promising to fulfill the need for high resolution, high sensitivity and quantitative nuclear medicine imaging. All the techniques developed in this work are readily applicable to multiple energy window acquisition in scintigraphic or SPECT imaging

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

Blind image deconvolution: nonstationary Bayesian approaches to restoring blurred photos

High quality digital images have become pervasive in modern scientific and everyday life — in areas from photography to astronomy, CCTV, microscopy, and medical imaging. However there are always limits to the quality of these images due to uncertainty and imprecision in the measurement systems. Modern signal processing methods offer the promise of overcoming some of these problems by postprocessing these blurred and noisy images. In this thesis, novel methods using nonstationary statistical models are developed for the removal of blurs from out of focus and other types of degraded photographic images. The work tackles the fundamental problem blind image deconvolution (BID); its goal is to restore a sharp image from a blurred observation when the blur itself is completely unknown. This is a “doubly illposed” problem — extreme lack of information must be countered by strong prior constraints about sensible types of solution. In this work, the hierarchical Bayesian methodology is used as a robust and versatile framework to impart the required prior knowledge. The thesis is arranged in two parts. In the first part, the BID problem is reviewed, along with techniques and models for its solution. Observation models are developed, with an emphasis on photographic restoration, concluding with a discussion of how these are reduced to the common linear spatially-invariant (LSI) convolutional model. Classical methods for the solution of illposed problems are summarised to provide a foundation for the main theoretical ideas that will be used under the Bayesian framework. This is followed by an indepth review and discussion of the various prior image and blur models appearing in the literature, and then their applications to solving the problem with both Bayesian and nonBayesian techniques. The second part covers novel restoration methods, making use of the theory presented in Part I. Firstly, two new nonstationary image models are presented. The first models local variance in the image, and the second extends this with locally adaptive noncausal autoregressive (AR) texture estimation and local mean components. These models allow for recovery of image details including edges and texture, whilst preserving smooth regions. Most existing methods do not model the boundary conditions correctly for deblurring of natural photographs, and a Chapter is devoted to exploring Bayesian solutions to this topic. Due to the complexity of the models used and the problem itself, there are many challenges which must be overcome for tractable inference. Using the new models, three different inference strategies are investigated: firstly using the Bayesian maximum marginalised a posteriori (MMAP) method with deterministic optimisation; proceeding with the stochastic methods of variational Bayesian (VB) distribution approximation, and simulation of the posterior distribution using the Gibbs sampler. Of these, we find the Gibbs sampler to be the most effective way to deal with a variety of different types of unknown blurs. Along the way, details are given of the numerical strategies developed to give accurate results and to accelerate performance. Finally, the thesis demonstrates state of the art results in blind restoration of synthetic and real degraded images, such as recovering details in out of focus photographs

Estimation of the Direct-Path Relative Transfer Function for Supervised Sound-Source Localization

International audienceThis paper addresses the problem of binaural localization of a single speech source in noisy and reverberant environments. For a given binaural microphone setup, the binaural response corresponding to the direct-path propagation of a single source is a function of the source direction. In practice, this response is contaminated by noise and reverberations. The direct-path relative transfer function (DP-RTF) is defined as the ratio between the direct-path acoustic transfer function of the two channels. We propose a method to estimate the DP-RTF from the noisy and reverberant microphone signals in the short-time Fourier transform domain. First, the convolutive transfer function approximation is adopted to accurately represent the impulse response of the sensors in the STFT domain. Second, the DP-RTF is estimated by using the auto-and cross-power spectral densities at each frequency and over multiple frames. In the presence of stationary noise, an inter-frame spectral subtraction algorithm is proposed, which enables to achieve the estimation of noise-free auto-and cross-power spectral densities. Finally, the estimated DP-RTFs are concatenated across frequencies and used as a feature vector for the localization of speech source. Experiments with both simulated and real data show that the proposed localization method performs well, even under severe adverse acoustic conditions, and outperforms state-of-the-art localization methods under most of the acoustic conditions