43913114
Second-generation error concealment for video transport over error-prone channels
Video transport over error-prone channels may result in loss or erroneous decoding of the video data. Error concealment is an effective mechanism to reconstruct the video data. In this paper, we review error-concealment methods and introduce a new framework, which we refer to as second-generation error concealment. All the error-concealment methods reconstruct the lost video data by making use of certain a priori knowledge about the video content. First-generation error concealment builds such a priori in a heuristic manner. The proposed second-generation error concealment builds the a priori by modeling the statistics of the video content explicitly, typically in the region of interest (ROI). Context-based models are trained with the correctly received video data and then used to replenish the lost video data. Trained models capture the statistics of the video content and thus reconstruct the lost video data better than reconstruction by heuristics. A new dynamic model ‘updating principal components’ (UPC) is proposed as a model for second-generation error concealment. UPC can be applied to pixel values to conceal loss of pixel data. In addition, UPC can be applied to motion vectors, which results in ‘updating eigenflows’ (U-Eigenflow), to conceal loss of motion vectors. With UPC applied to both pixel values and motion vectors, hybrid temporal/spatial error concealment can be achieved. The proposed second-generation error-concealment method provides superior performances to first-generation error-concealment methods. Copyright © 2002 John Wiley & Sons, Ltd.
9048292
Updating mixture of principal components for error concealment
We present a new statistical modeling technique called "updating mixture of principal components" (UMPC). UMPC specifically captures the non-stationary as well as the multi-modal characteristics of the data. Real-world data such as video data typically have these two characteristics. The video content changes over time and has a multi-modal probability distribution. We apply UMPC to perform error concealment for video data transmitted over networks with losses, and show that UMPC outperforms conventional error concealment methods.
418775
Progressive image watermarking
Progressive transmission of images is very useful in many applications, especially in image transmission over the Internet. To view an image, people would want to see part of the image while the image transmission is in progress, rather than having to wait until the end of the image transmission. On the other hand, the ease of transmission and copying of images creates the need to use digital watermarking to embed the copyright information seamlessly into the media. We propose a progressive image watermarking scheme. In this scheme, the watermark is embedded in such a way that we can retrieve part of it even when the watermarked image is still being transmitted. As transmission progresses, the retrieved watermark has a decreasing bit error rate. Our proposed methods not only transmit the watermarked image progressively, but also intelligently select watermark embedding locations robust to various attacks.
17692093
Life after Video Coding Standards: Rate Shaping and Error Concealment
Is there life after video coding standards? One might think that research has no room to advance with the video coding standards already defined. On the contrary, exciting research opportunities arise after the standards are specified. In this paper, we introduce two standard-related research areas: rate shaping and error concealment, as examples of interesting research that finds its context in standards. Experiment results are also shown.
5643714
On Some Characterisation Problems of Subdomain Testing
Subdomain testing is a very general approach to the selection of test cases. It captures the characteristics of testing strategies that require the test suite to cover some predefined testing requirements. This paper attempts to characterise precisely the failure distributions for the best and worst case of any given subdomain testing strategy. Our analysis has revealed some crucial factors and principles that affect the effectiveness of subdomain testing strategies.
10160655
A Suboptimal Algorithm for De Novo Peptide Sequencing via Tandem Mass Spectrometry
Tandem mass spectrometry has emerged to be one of the most powerful high-throughput techniques for protein identification. Tandem mass spectrometry selects and fragments peptides of interest into N-terminal ions and C-terminal ions, and it measures the mass/charge ratios of these ions. The de novo peptide sequencing problem is to derive the peptide sequences from given tandem mass spectral data of k ion peaks without searching against protein databases. By transforming the spectral data into a matrix spectrum graph G = (V, E), where |V| = O(k(2)) and |E| = O(k(3)), we give the first polynomial time suboptimal algorithm that finds all the suboptimal solutions (peptides) in O(p|E|) time, where p is the number of solutions. The algorithm has been implemented and tested on experimental data. The program is available at http://hto-c.usc.edu:8000/msms/menu/denovo.htm.
17694458
Optimal broadcasting on incomplete star graph interconnection networks
Broadcasting is an important collective communication operation in many applications which use parallel computing. In this paper, we focus on designing broadcasting algorithms for general incomplete star graphs. We propose two optimal one-to-all broadcasting algorithms for incomplete star graphs with a single-port communication model. An incomplete star graph with N nodes, where (n - 1)! < N < n!, is a subgraph of an n-dimensional star graph. The first scheme is single-message one-to-all broadcasting that takes O(nlogn) steps. The second one is multi-message one-to-all broadcasting that takes O(nlogn + m) steps.
8315237
Local Lazy Regression: Making Use of the Neighborhood to Improve QSAR Predictions
Traditional quantitative structure-activity relationship (QSAR) models aim to capture global structure-activity trends present in a data set. In many situations, there may be groups of molecules which exhibit a specific set of features which relate to their activity or inactivity. Such a group of features can be said to represent a local structure-activity relationship. Traditional QSAR models may not recognize such local relationships. In this work, we investigate the use of local lazy regression (LLR), which obtains a prediction for a query molecule using its local neighborhood, rather than considering the whole data set. This modeling approach is especially useful for very large data sets because no a priori model need be built. We applied the technique to three biological data sets. In the first case, the root-mean-square error (RMSE) for an external prediction set was 0.94 log units versus 0.92 log units for the global model. However, LLR was able to characterize a specific group of anomalous molecules with much better accuracy (0.64 log units versus 0.70 log units for the global model). For the second data set, the LLR technique resulted in a decrease in RMSE from 0.36 log units to 0.31 log units for the external prediction set. In the third case, we obtained an RMSE of 2.01 log units versus 2.16 log units for the global model. In all cases, LLR led to a few observations being poorly predicted compared to the global model. We present an analysis of why this was observed and possible improvements to the local regression approach.
8821639
Realistic video avatar
We present a system for the implementation of a photorealistic avatar using video captured from a user. This is achieved by constructing the dynamic video texture map and combining it with the 3D mesh model of the user to render the photorealistic avatar. The dynamic video texture map reflects the user's facial expressions and is generated by composing the cylindrical texture map of the user with constant updates either directly from the input video or derived from a temporal and spatial interpolation scheme. To derive the 3D mesh model for the user, we define feature points on a generic model and use the feature points identified on the video sequence to deform and obtain the avatar model. The goal of this realistic video avatar project is to provide a vivid representation of participants with a more realistic quality avatar, as compared to using only the facial animation parameters defined in MPEG-4 without the corresponding image updates. This scheme is suitable for conferencing applications because it requires much lower bandwidth than live video, and yet provides a 3D avatar representation for any virtual environment.