Packet Video Error Concealment With Auto Regressive Model

During the propagation of seismic waves underground, the high-frequency seismic response of thin reservoir is absorbed and attenuated, which poses a challenge in seismic thin reservoir prediction. The high-resolution processing techniques have the capability to significantly expand the frequency range of the seismic data, so it becomes a key technique for thin reservoir prediction. Most of these techniques necessitate the extraction of seismic wavelets. However, the spatial and temporal variations of seismic data result in multiple solutions for wavelet extraction. Simultaneously, the majority of techniques fail to consider the influence of spatial tectonic features on the high-resolution processing. In this paper, we propose a novel solution to address these two fundamental challenges by utilizing seismic spectral expansion, sparse reflection coefficients, and spatial continuity constraints. First, we propose an innovative spectral fitting method that aims to expand the frequency bandwidth while adhering to the desired wavelet constraints. This method allows us to fully utilize the effective frequency information. It not only obtains broadband seismic data but also captures precise wavelets. Then, sparse deconvolution is employed to further extend the frequency range by utilizing the accurately expected wavelet and obtaining a high-resolution reflection coefficient. Finally, the Hessian matrix regularization is employed to constrain the spatial continuity of the reflection coefficient. This method is validated in both the model and real seismic data. Compared to traditional sparse deconvolution and spectral modeling deconvolution with spatial constraints, this method not only expands the frequency bandwidth and enhances seismic resolution but also preserves operational frequency information and improves the spatial continuity of seismic data. It has been verified that this approach can be used to forecast thin reservoir and reconstruct spatial tectonic characteristics.

Error concealment is a well-known technique to improve reproduced picture quality in cases which parts of the coded picture are not available at a decoder for reconstruction. The conventional error concealment methods generally make use of the correlation between a damaged marcoblock and its adjacent macroblocks in the same frame and/or the previous frame. However, performance results of concealment methods are different each other and dependent on how much damaged frames and macroblocks are correlated to surrounding ones temporally and spatially. Moreover, most of them can not conceal effectively the consecutively damaged frames or macro blocks caused by wireless bursty packet loss features, since motion vectors and correlation information between the damaged images are already destroyed. The proposed error concealment aware error resilient video coding is a new method which has an intelligent error concealment selector by using pre-estimation technique of the possibly damaged motion vectors at the encoder. The method recommends the best error concealment per macroblock by transmitting additionally its error concealment selection codes to the decoder. The chosen error concealment helps to conceal the damaged MB at the decoder even when losses of frames are bursty. The experimental results show the substantial effectiveness to improve video quality at the cost of a little bit overhead at the transmitted bit streams.

Non-negative matrix factorization (NMF) is widely used for music transcription because of its efficiency. However, the conventional NMF-based music transcription algorithm often causes harmonic confusion errors or time split-up errors, because the NMF decomposes the time-frequency data according to the activated frequency in its time. To solve these problems, we proposed an NMF with temporal continuity and harmonicity constraints. The temporal continuity constraint prevented the time split-up of the continuous time components, and the harmonicity constraint helped to bind the fundamental with harmonic frequencies by reducing the additional octave errors. The transcription performance of the proposed algorithm was compared with that of the conventional algorithms, which showed that the proposed method helped to reduce additional false errors and increased the overall transcription performance.

This paper presents a new blind direction of arrival (DOA) estimation approach for closely-spaced sources. The new method first estimates the autoregressive (AR) coefficients via an initial DOA estimation and then uses the AR coefficients for the linear extrapolation of the correlation matrix to implement a fine DOA estimation. Both initial and fine DOA estimations are performed using the estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. Unlike a conventional AR coefficient estimation method which estimates the AR coefficients on the snapshot basis, our AR coefficients are estimated in the correlation domain once for a block of snapshots, thus significantly reducing the computational complexity of the antenna array. Moreover, the proposed spatial extrapolation-based DOA estimation approach is analyzed using perturbation theory. Both the theoretical analysis and computer simulations show that the proposed method outperforms the conventional techniques in terms of the mean square error (MSE) of the DOA estimation when the angle of separation of the signal sources is very small.

Radial velocity data of Doppler weather radars are crucial for wind field retrieval and nowcasting, but multi-fold velocity aliasing in short-wavelength radar systems (e.g., X/Ku-band) severely restricts their application. Conventional velocity dealiasing methods relying on velocity continuity fail in such scenarios due to unreliable initial reference velocity determination. To resolve this challenge, the Morphology-based Zero Iso-Doppler Contour (Zero-Isodop) Extraction and Velocity Correction Algorithm (MZI-VCA) is proposed for multi-fold velocity-aliased weather radar. The method first constructs a large-scale uniform horizontal wind model to characterize aliased zero-isodop distributions, then applies morphological operations combined with Depth-First Search (DFS) for candidate isodop extraction in Plan Position Indicator (PPI) images, and finally identifies the zero-isodop through spatial structure metrics. Leveraging the extracted zero-isodop, dealiasing is performed under spatial continuity constraints. Validations using both simulated multi-fold aliasing data (derived from S-band radar) and Ku-band weather radar observations demonstrate that MZI-VCA achieves superior performance compared to the traditional dealiasing method UNRAVEL. MZI-VCA achieves 100% dealiasing success rate in correcting double- and triple-fold aliasing, with a success rate of 90.74% across all diverse multi-fold scenarios tested in 108 simulated volume scans. In contrast, UNRAVEL fails to resolve multi-fold aliasing, exhibiting only a 6.92% Probability of Detection (POD). The systematic method establishes a new framework for resolving multi-fold velocity aliasing in short-wavelength radar systems.

We present an error-concealed embedded wavelet (ECEW) video coding system for transmission over Internet or wireless networks. This system consists of two types of frames: intra (I) frames and inter, or predicted (P), frames. Inter frames are constructed by the residual frames formed by variable block-size multiresolution motion estimation (MRME). Motion vectors are compressed by arithmetic coding. The image data of intra frames and residual frames are coded by error-resilient embedded zerotree wavelet (ER-EZW) coding. The ER-EZW coding partitions the wavelet coefficients into several groups and each group is coded independently. Therefore, the error propagation effect resulting from an error is only confined in a group. In EZW coding any single error may result in a totally undecodable bitstream. To further reduce the error damage, we use the error concealment at the decoding end. In intra frames, the erroneous wavelet coefficients are replaced by neighbors. In inter frames, erroneous blocks of wavelet coefficients are replaced by data from the previous frame. Simulations show that the performance of ECEW is superior to ECEW without error concealment by 7 to approximately 8 dB at the error-rate of 10<SUP>-3</SUP> in intra frames. The improvement still has 2 to approximately 3 dB at a higher error-rate of 10<SUP>-2</SUP> in inter frames.

Motion-vector-based error concealment algorithms recover lost blocks by motion compensation with the estimated motion vectors. Traditionally, the estimated motion vectors for the lost block are derived by the direct usage of the neighboring available motion vectors. In this work, the residual signal in a block encoded in the video bitstream is used to measure the reliability of the corresponding motion vector. The residual information is applied to weight the neighboring available motion vectors differently, to further improve a newly proposed mathematical formulation between encoded motion vectors and extrapolated motion vectors. The proposed error concealment method using the residual information outperforms a state-of-the-art error concealment algorithm by up to 2.12 dB in sequence PSNR, and outperforms a classic DMVE method by up to 8.27 dB.

Adding data process feedback to the nonlinear autoregressive model

In this paper, a graph-based method for video scene detection is proposed. The method is based on a weighted undirected graph. Each shot is a vertex on the graph. Edge weights among the vertices are evaluated by using spatial and temporal similarities of shots. By using the complete information of the graph, a set of the vertices mostly similar to each other and dissimilar to the others is detected. Temporal continuity constraint is achieved on this set. This set is the first detected video scene. The vertices of the video scene are extracted from the graph and the process is repeated by a certain number. The certain number of the video scenes whose boundaries are determined are placed in the temporal domain. Each temporal part between two detected scenes is accepted as a video scene.

We present PEFAC, a fundamental frequency estimation algorithm for speech that is able to identify voiced frames and estimate pitch reliably even at negative signal-to-noise ratios. The algorithm combines a normalization stage, to remove channel dependency and to attenuate strong noise components, with a harmonic summing filter applied in the log-frequency power spectral domain, the impulse response of which is chosen to sum the energy of the fundamental frequency harmonics while attenuating smoothly-varying noise components. Temporal continuity constraints are applied to the selected pitch candidates and a voiced speech probability is computed from the likelihood ratio of two classifiers, one for voiced speech and one for unvoiced speech/silence. We compare the performance of our algorithm with that of other widely used algorithms and demonstrate that it performs well in both high and low levels of additive noise.

This paper presents a classification method for arrhythmia using probabilistic neural network, based on unique combination of three Electrocardiogram (ECG) features; heart rate, Auto Regressive (AR) coefficients & spectral entropy (SE). Heart rate has been very critical parameter for the detection of life-threatening arrhythmia. The purpose of this paper is to develop a Probabilistic Neural Network (PNN) based algorithm for improved detection and broader classification of cardiac arrhythmia. The results show that the unique combination of ECG features considered in this work provides more accurate and robust classification of arrhythmias.

Near Infra-Red Spectroscopy (NIRS) is possible to measure brain activity signals with low invasive and low physical restraint, so it is also expected as a useful clinical tool in the fields of medicine and education. However, measurement and detection of weak signal of brain activities such as human intention and recall of memory, is very difficult because noise signals such as heartbeat, respiration, and body movement disturb the brain activity signal and data. In this study, we attempt to reduce the effect of the artifacts ingredient by the body movement from the data of NIRS using the Auto- Regressive (AR) model. From the experimental results, we confirmed that the peaks of FFT analysis of brain activity measurement data and ECG data were almost identical. Particularly, characteristic peaks around 0.5 Hz and 1.3 Hz were detected. The peak around 0.5 Hz was effected based on position change of the head inclining. The peak around 1.3 Hz was effected based on the periodical R wave in the ECG data. Also, AR model was estimated from AR coefficients by the above results. So, we could separate the component of artifacts based on heartbeats and separate the component of artifacts based on positioning change of the head inclining, from the brain activity signals. It means that using AR model from the brain activity measurement data by NIRS, we could reduce the effects of artifacts based on heartbeats and reduce the effects artifacts based on position change of the head inclining.

In the previous chapters, we discussed autoregressive linear mixed effects models. In this section, we discuss the relationships between the autoregressive linear mixed effects models and nonlinear mixed effects models, growth curves, and differential equations. The autoregressive model shows a profile approaching an asymptote, where the change is proportional to the distance remaining to the asymptote. Autoregressive models in discrete time correspond to monomolecular curves in continuous time. Autoregressive linear mixed effects models correspond to monomolecular curves with random effects in the baseline and asymptote, and special error terms. The autoregressive coefficient is a nonlinear parameter, but all random effects parameters in the model are linear. Therefore, autoregressive linear mixed effects models are nonlinear mixed effects models without nonlinear random effects and have a closed form of likelihood. When there are time-dependent covariates, autoregressive linear mixed effects models are represented by a differential equation and random effects. The monomolecular curve is one of the popular growth curves. We introduce other growth curves, such as the logistic curves and von Bertalanffy curves, and generalizations of growth curves. Re-parameterization is often performed in nonlinear models, and various representations of re-parameterization in monomolecular and other curves are provided herein.

Side information generation with auto regressive model for low-delay distributed video coding

In temporal error concealment (EC), the sum of absolute difference (SAD) is commonly used to identify the best replacement macroblock. Even though the use of SAD ensures spatial continuity and produces visually good results, it is insufficient to ensure edge alignment. Other distortion criteria based solely on structural alignment may also perform poorly in the absence of strong edges. In this paper, we propose a spatio-temporal EC search algorithm using an edge weighted SAD distortion criterion. This distortion criterion ensures both edge alignment and spatial continuity. We assume the loss of motion information and use zero motion vector as the starting search point. We show that the proposed algorithm outperforms the use of unweighted SAD in general. Most importantly, the perceptual quality of EC is improved due to edge alignment while ensuring spatial continuity.