16950996
Joint source-channel coding for wireless object-based video communications utilizing data hiding
In recent years, joint source-channel coding for multimedia communications has gained increased popularity. However, very limited work has been conducted to address the problem of joint source-channel coding for object-based video. In this paper, we propose a data hiding scheme that improves the error resilience of object-based video by adaptively embedding the shape and motion information into the texture data. Within a rate-distortion theoretical framework, the source coding, channel coding, data embedding, and decoder error concealment are jointly optimized based on knowledge of the transmission channel conditions. Our goal is to achieve the best video quality as expressed by the minimum total expected distortion. The optimization problem is solved using Lagrangian relaxation and dynamic programming. The performance of the proposed scheme is tested using simulations of a Rayleigh-fading wireless channel, and the algorithm is implemented based on the MPEG-4 verification model. Experimental results indicate that the proposed hybrid source-channel coding scheme significantly outperforms methods without data hiding or unequal error protection
15384375
A hybrid source-channel coding scheme for object-based wireless video communications
In this paper, we study the joint source-channel coding of object-based video, and propose a data hiding scheme that improves the video error resilience by adaptively embedding the shape and motion information in the texture data. Within a rate-distortion theoretical framework, the source coding, channel coding, data embedding, and decoder error concealment are jointly optimized based on the knowledge of transmission channel conditions. The problem is solved using Lagrangian relaxation and dynamic programming. Experimental results indicate that the proposed hybrid source-channel coding scheme significantly outperforms methods without data hiding or unequal error protection
14473430
An optimal shape encoding scheme using skeleton decomposition
This paper presents an operational rate-distortion (ORD) optimal approach for skeleton-based boundary encoding. The boundary information is first decomposed into skeleton and distance signals, by which a more efficient representation of the original boundary results. Curves of arbitrary order are utilized for approximating the skeleton and distance signals. For a given bit budget for a video frame, we solve the problem of choosing the number and location of the control points for all skeleton and distance signals and for all boundaries within a frame, so that the overall distortion is minimized. The problem is solved with the use of Lagrangian relaxation and a shortest path algorithm in a 4D directed acyclic graph (DAG) we propose. By defining a path selection pattern, we reduce the computational complexity of the 4D DAG shortest path algorithm from O(N/sup -5/) to O(N/sup -4/), where N is the number of admissible control points for a skeleton. A suboptimal solution is also presented for further reducing the computational complexity of the algorithm to O(N/sup -2/). The proposed algorithm outperforms experimentally other competing algorithms.
11552771
Rate-distortion optimal skeleton-based shape coding
We present a new shape-coding approach, which decouples the shape information into two independent data sets, the skeleton and the distance of the boundary from the skeleton. The major benefit of this approach is that it allows a more flexible trade-off between accuracy of the approximation and bit-allocation cost, and thus, provides the possibility of better performance in the operational rate-distortion (ORD) optimal sense than other reported techniques. The characteristics of these data sets are studied and various approximation approaches are applied on each of them to reach an ORD optimal result. We apply, for example, polygonal approximation on both the skeleton and distance data. We demonstrate that the resulting approach outperforms existing ORD optimal approaches.
7895439
An efficient rate-distortion optimal shape coding approach utilizing a skeleton-based decomposition
In this paper, we present a new shape-coding approach, which decouples the shape information into two independent signal data sets; the skeleton and the boundary distance from the skeleton. The major benefit of this approach is that it allows for a more flexible tradeoff between approximation error and bit budget. Curves of arbitrary order can be utilized for approximating both the skeleton and distance signals. For a given bit budget for a video frame, we solve the problem of choosing the number and location of the control points for all skeleton and distance signals of all boundaries within a frame, so that the overall distortion is minimized. An operational rate-distortion (ORD) optimal approach using Lagrangian relaxation and a four-dimensional direct acyclic graph (DAG) shortest path algorithm is developed for solving the problem. To reduce the computational complexity from O(N(5)) to O(N(3)), where N is the number of admissible control points for a skeleton, a suboptimal greedy-trellis search algorithm is proposed and compared with the optimal algorithm. In addition, an even more efficient algorithm with computational complexity O(N(2)) that finds an ORD optimal solution using a relaxed distortion criterion is also proposed and compared with the optimal solution. Experimental results demonstrate that our proposed approaches outperform existing ORD optimal approaches, which do not follow the same decomposition of the source data.
11673267
Object-based video compression scheme with optimal bit allocation among shape, motion and texture
In object-based video, the encoding of the video data is decoupled into the encoding of shape, motion and texture information, which enables certain functionalities like content-based interactivity and scalability. However, the problem of how to jointly encode these separate signals to reach the best coding efficiency has never been solved thoroughly. In this paper, we present an operational rate-distortion optimal bit allocation scheme that provides a solution to this problem. Our approach is based on the Lagrangian relaxation and dynamic programming. Experimental results indicate that the proposed optimal encoding approach has considerable gains over an ad-hoc method without optimization. Furthermore the proposed algorithm is much more efficient than exhaustive search.
16600904
A services oriented framework for next generation data analysis centers
Over the past decade, advances in computational and sensor technology have enabled us to dynamically collect vast amounts of data from observations, health screening tests, simulations, and experiments at an ever-increasing pace. Knowledge discovery and data mining is an iterative process concerned with deriving interesting, non-obvious, and useful patterns and models from such large volumes of data. Although inexpensive storage is conducive to maintaining said data, accessing and managing it for knowledge discovery and data mining becomes a performance issue when datasets are large, dynamic, and distributed. In this work, we present our vision of a software framework consisting of middleware services to support interactive data mining over dynamic data at data analysis centers built on top of heterogeneous clusters. The design of a sampling service for dynamic data, together with initial performance results, are also presented.
1542177
Out-of-core tensor approximation of multi-dimensional matrices of visual data
Tensor approximation is necessary to obtain compact multilinear models for multi-dimensional visual datasets. Traditionally, each multi-dimensional data item is represented as a vector. Such a scheme flattens the data and partially destroys the internal structures established throughout the multiple dimensions. In this paper, we retain the original dimensionality of the data items to more effectively exploit existing spatial redundancy and allow more efficient computation. Since the size of visual datasets can easily exceed the memory capacity of a single machine, we also present an out-of-core algorithm for higher-order tensor approximation. The basic idea is to partition a tensor into smaller blocks and perform tensor-related operations blockwise. We have successfully applied our techniques to three graphics-related data-driven models, including 6D bidirectional texture functions, 7D dynamic BTFs and 4D volume simulation sequences. Experimental results indicate that our techniques can not only process out-of-core data, but also achieve higher compression ratios and quality than previous methods.
10755440
Syllable-Based Chinese Text/Spoken Document Retrieval Using Text/Speech Queries
In light of the rapid growth of Chinese information resources on the Internet, this study investigates a novel approach that deals with the problem of Chinese text and spoken document retrieval using both text and speech queries. By properly utilizing the monosyllabic structure of the Chinese language, the proposed approach estimates the statistical similarity between the text/speech queries and the text/spoken documents at the phonetic level using the syllable-based statistical information. The investigation successfully implemented a prototype system with an interface supporting some user-friendly functions and the initial test results demonstrate the feasibility of the proposed approach.
14876128
A native extension of SQL for mining data streams
ESL1 enables users to develop stream applications in an SQL-like high level language that provides the ease-of-use of a declarative language, which is Turing complete in terms of expressive power [11].
1492261
Object Tracking with Particle Filter Using Color Information
Color-based particle filter for object tracking has been an active research topic in recent years. Despite great efforts of many researchers, there still remains to be solved the problem of contradiction between efficiency and robustness. The paper makes an attempt to partially solve this problem. Firstly, the Integral Histogram Image is introduced by which histogram of any rectangle region can be computed at negligible cost. However, straightforward application of the Integral Histogram Images causes the problem of “curse of dimensionality”. In addition, traditional histogram is inefficient and inaccurate. Thus we propose to adaptively determine histogram bins based on K-Means clustering, which can represent color distribution of object more compactly and accurately with as a small number of bins. Thanks to the Integral Histogram Images and the clustering based color histogram, we finally achieve a fast and robust particle filter algorithm for object tracking. Experiments show that the performance of the algorithm is encouraging.