1210599
Joint error concealment and error recovery for consecutive frame losses under the unbalanced multiple description coding architecture
Real-time video transmission over error-prone wireless networks often experiences consecutive frame losses due to either temporary link outages or traffic congestion. Although error concealment (EC) techniques have been extensively studied, they usually cannot handle the problem. Thus, we envision using EC under the unbalanced multiple description coding (UMDC) architecture. UMDC has almost no coding delay and can produce two descriptions at any bit-rates adaptive to different path bandwidths. In this paper, we propose an iterative EC algorithm able to adaptively exploit both the high-resolution (HR) and low-resolution (LR) information via multi-hypothesis weights. It is applied to both lost HR frames and following undecoded ones. Considering error propagation, we design an interframe error recovery (ER) algorithm for the undecoded HR frames. It iteratively uses multi-frame recovery principle to frame-by-frame reduce error drift in the HR stream with respect to the intermediate information from EC. The joint design of EC and ER can be applied to most UMD approaches. Extensive experiments have been carried out under different conditions. The proposed EC technique exhibits high PSNR gains versus the usual ones under the UMDC architecture and the classical ones without the support of UMDC. The proposed ER technique is efficient in reducing error drift. In conclusion, joint EC and ER can provide satisfactory performance on both PSNR and visual quality.
38238585
Exploiting multi-scale support vector regression for image compression
Unlike traditional neural networks that require predefined topology of the network, support vector regression (SVR) approach can model the data within the given level of accuracy with only a small subset of the training data, which are called support vectors (SVs). This property of sparsity has been exploited as the basis for image compression. In this paper, for still image compression, we propose a multi-scale support vector regression (MS-SVR) approach, which can model the images with steep variations and smooth variations very well resulting in good performance. We test our proposed MS-SVR based algorithm on some standard images. The experimental results verify that the proposed MS-SVR achieves better performance than standard SVR. And in a wide range of compression ratio, MS-SVR is very close to JPEG in terms of peak signal-to-noise ratio (PSNR) but exhibits better subjective quality. Furthermore, MS-SVR even outperforms JPEG on both PSNR and subjective quality when the compression ratio is higher enough, for example 25:1 for Lena image. Even when compared with JPEG-2000, the results show greatly similar trend as those in JPEG experiments, except that the compression ratio is a bit higher where our proposed MS-SVR will outperform JPEG-2000.
709845
Supporting Live Video on Ad Hoc Wireless Networks: Unbalanced Multiple Description Coding, Single Path Transport and Recursive Error Concealment
Robust transmission of live video over ad hoc wireless networks presents new challenges: high bandwidth requirements are coupled with delay constraints; nodes are constrained in processing power and storage capacity; ad hoc wireless networks suffer from bursty packet losses that drastically degrade the viewing experience. Accordingly, we propose a simple, but practical unbalanced multiple description (UMD) codec that uses single path transport only. At the UMD decoder, the lost Low- Resolution (LR) frames can be receded from the corresponding ones of the correctly received High- Resolution (HR) stream. This makes LR more robust to packet losses and ensures continuous video playback. The simulation results show that the proposed UMD codec has higher decoded quality, smaller quality fluctuation and lower probability of pause than other error resilience techniques, especially when channel burstiness becomes large. We also propose a novel sequence-based error concealment (EC) algorithm for our UMD decoder. It recursively uses multi-frame recovery principle to frame-by-frame reduce error drift in the HR description with respect to the LR description. In fact, the algorithm can be applied to most UMD schemes. It has the advantage of working on integer-, half- or quarter- pixel precision and looking backward without buffering future frames. Experimental results show that error decreases the fastest if the UMD decoder uses our EC algorithm. It can provide satisfactory performance on both objective and subjective evaluation.
8253940
A pinhole camera modeling of motion vector field for tennis video analysis
The motion vector field (MVF) represents motion characteristics in video sequences, and has been widely used and been proved to be effective in sports video analysis. However, in tennis video analysis, MVF-based methods are seldom utilized for the reasons that: (i) the players' true motion is not accurately represented by the extracted motion vector, due to the deformation caused by the diagonal shooting of the camera; and (ii) the motion vector's magnitude is not prominent enough and is prone to be disturbed by noises. In this paper, a pinhole camera modeling of the motion vector field is proposed to revise the deformed motion vector. In this modeling, a foreground object mask is adopted and global motion compensation is incorporated as the pre-processing steps. Evaluation of the proposed modeling using four hours of tennis videos shows very encouraging results.
28617398
An Unbalanced Multiple Description Coding Scheme for Video Transmission Over Wireless Ad Hoc Networks
5815107
SIKAS: A Scalable Distributed Key Management Scheme for Dynamic Collaborative Groups
The increasing popularity of distributed and collaborative applications prompts the need for secure communication in collaborative groups. Some distributed collaborative key management protocols have been proposed to provide group communication privacy and data confidentiality for collaborative groups. However, most of them rekey on each member change, and the costs of group rekeying can be quite substantial for large groups with frequent membership changes. In this paper, we propose a scalable distributed key management scheme using a distributed one-way function tree named SIKAS which can significantly reduce the computation and communication costs of maintaining the group key based upon period-based group rekeying. A comparison with previous work has shown that SIKAS provides scalability and rekeying efficiency while preserving both distributed and collaborative properties
16922329
A New Transmit Scheme Combining Beamforming with Space-Time Block Coding
In this paper, a new transmit scheme combining beamforming (BF) with space-time block coding (STBC) is proposed for correlated fading channels. Based on maximizing the output mean signal-to-noise ratio (SNR) at the receiver, the transmitter BF weight vectors are first derived. Then, utilizing the minimal bit error rate (BER) upper bound as the design criterion, a simple power allocation algorithm is developed. Next, the BER performance of the system with the proposed transmit scheme is analyzed by considering an M-QAM constellation. Finally, computer simulation results are given to verify the effectiveness of the proposed scheme.
2524422
Versatile prediction and fast estimation of Architectural Vulnerability Factor from processor performance metrics
The shrinking processor feature size, lower threshold voltage and increasing clock frequency make modern processors highly vulnerable to transient faults. Architectural Vulnerability Factor (AVF) reflects the possibility that a transient fault eventually causes a visible error in the program output, and it indicates a system's susceptibility to transient faults. Therefore, the awareness of the AVF especially at early design stage is greatly helpful to achieve a trade-off between system performance and reliability. However, tracking the AVF during program execution is extremely costly, which makes accurate AVF prediction extraordinarily attractive to computer architects. In this paper, we propose to use Boosted Regression Trees, a nonparametric tree-based predictive modeling scheme, to identify the correlation across workloads, execution phases and processor configurations between a key processor structure's AVF and various performance metrics. The proposed method not only makes an accurate prediction but quantitatively illustrates individual performance variable's importance to the AVF. Moreover, to reduce the prediction complexity, we also utilize a technique named Patient Rule Induction Method to extract some simple selecting rules on important metrics. Applying these rules during run time can fast identify execution intervals with a relatively high AVF.
26704614
A New Approach to Forecast Crude Oil Price Based on Fuzzy Neural Network
A new oil price forecasting model based on fuzzy neural network, which combings RBF neural network, Markov chain based semi parametric model and wavelet analysis is proposed in this paper. The high degree of prediction accuracy confirms the performance of the model in accurate forecasts, reinforcement learning properties and mapping capabilities.
16377263
Personal Name Recognition Based on Categorized Linguistic Knowledge
This paper proposes an integrated approach for personal name recognition (PNR) in Chinese by utilizing both statistical language models and categorized linguistic knowledge. Various formulas are proposed for calculating personal name credibility and context credibility for different types of personal names. Experiment is conducted on large-scale corpus to evaluate the approach and the F-1 scores has reached 98.85% and 92.73% respectively in close and open test.
14221074
Accurate and efficient processor performance prediction via regression tree based modeling
Computer architects usually evaluate new designs using cycle-accurate processor simulation. This approach provides a detailed insight into processor performance, power consumption and complexity. However, only configurations in a subspace can be simulated in practice due to long simulation time and limited resource, leading to suboptimal conclusions which might not be applied to a larger design space. In this paper, we propose a performance prediction approach which employs state-of-the-art techniques from experiment design, machine learning and data mining. According to our experiments on single and multi-core processors, our prediction model generates highly accurate estimations for unsampled points in the design space and show the robustness for the worst-case prediction. Moreover, the model provides quantitative interpretation tools that help investigators to efficiently tune design parameters and remove performance bottlenecks.