Digital video databases in compressed form are becoming widely available. In applications such as video browsing, and picture in picture, for a lower bitrate there is a need to down-sample the video before transmissio...
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Digital video databases in compressed form are becoming widely available. In applications such as video browsing, and picture in picture, for a lower bitrate there is a need to down-sample the video before transmission. The conventional approach to downscale a compressed video sequence is to decompress it at the video server, perform the down-sampling in the pixel domain and recompress it again for efficient delivery. This process is computationally expensive due to the motion estimation process needed during the recompression phase. In the alternative compressed domain based approach the downscaled motion vectors are computed directly from the original ones. We propose a method to improve the process of downscaling in the compressed domain by better selecting the motion prediction type of P and B frame macroblocks. Simulations suggest that the proposed scheme improves the compressed domain down-sampling quality by up to 2.2 dB without any substantial increase in computation.
Often, images or datasets have to be compared, to facilitate choices of visualization and simulation parameters respectively. Common comparison techniques include side-by-side viewing and juxtaposition, in order to fa...
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ISBN:
(纸本)9780780358973
Often, images or datasets have to be compared, to facilitate choices of visualization and simulation parameters respectively. Common comparison techniques include side-by-side viewing and juxtaposition, in order to facilitate visual verification of verisimilitude. In this paper, we propose quantitative techniques which accentuate differences in images and datasets. The comparison is enabled through a collection of partial metrics which, essentially, measure the lack of correlation between the datasets or images being compared. That is, they attempt to expose and measure the extent of the inherent structures in the difference between images or datasets. Besides yielding numerical attributes, the metrics also produce images, which can visually highlight differences. Our metrics are simple to compute and operate in the spatial domain. We demonstrate the effectiveness of our metrics through examples for comparing images and datasets.
Digital video databases in compressed form are becoming widely available. In applications such as video browsing, and picture in picture, for a lower bitrate, there is a need to down-sample the video before transmissi...
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Digital video databases in compressed form are becoming widely available. In applications such as video browsing, and picture in picture, for a lower bitrate, there is a need to down-sample the video before transmission. The conventional approach to downscale a compressed video sequence is to decompress it at the video server, perform the down-sampling in the pixel domain and then recompress it for efficient delivery. This process is computationally intensive due to the motion estimation process required during the recompression phase. In the alternative compressed domain approach, the motion vectors of the downscaled video sequence are computed directly from the motion vectors of the original full size stream. In this paper we propose a compressed domain technique that generates a better estimate for the downscaled motion vectors. Simulations suggest that the performance achieved with the proposed method is superior by up to 1 dB to the current compressed domain techniques.
The FALCON development environment was designed around three basic data representations: scalars, vectors, and dense matrices. Utilizing the FALCON interactive restructuring system, the environment has been enhanced t...
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The FALCON development environment was designed around three basic data representations: scalars, vectors, and dense matrices. Utilizing the FALCON interactive restructuring system, the environment has been enhanced to allow the identification of structures within sparse matrices, such as diagonal matrices or symmetric matrices, and the use of this information for improving performance of the generated code. In addition, the environment supports the modification of the representation of the data. Such modifications have been shown to provide significant performance improvements.
Presents a detailed characterization of Java application and applet workloads in terms of reuse and sharing of Java code at the program, class and method level. In order to expose more sharing opportunities, technique...
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The nano-threads programming model was proposed to effectively integrate multiprogramming on shared-memory multiprocessors, with the exploitation of fine-grain parallelism from standard applications. A prerequisite fo...
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Autoscheduling is a parallel program compilation and execution model that combines uniquely three features: Automatic extraction of loop and functional parallelism at any level of granularity, dynamic scheduling of pa...
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ISBN:
(纸本)3540649522
Autoscheduling is a parallel program compilation and execution model that combines uniquely three features: Automatic extraction of loop and functional parallelism at any level of granularity, dynamic scheduling of parallel tasks, and dynamic program adaptability on multi-programmed shared memory multiprocessors. This paper presents a technique that enhances the performance of autoscheduling in Distributed Shared Memory (DSM) multiprocessors, targetting mainly at medium and large scale systems, where poor data locality and excessive communication impose performance bottlenecks. Our technique partitions the application Hierarchical Task Graph and maps the derived partitions to clusters of processors in the DSM architecture. Autoscheduling is then applied separately for each partition to enhance data locality and reduce communication costs. Our experimental results show that partitioning achieves remarkable performance improvements compared to a standard autoscheduling environment and a commercial parallelizing compiler.
Application development for high-performance distributed computing systems, or computational grids as they are sometimes called, requires "grid-enabled" tools that hide mundane aspects of the heterogeneous g...
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NYNET (ATM wide area network testbed in New York state) Communication System (NCS) is a multithreaded message passing system developed at Syracase University that provides highperformance and flexible communication s...
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NYNET (ATM wide area network testbed in New York state) Communication System (NCS) is a multithreaded message passing system developed at Syracase University that provides highperformance and flexible communication services over asynchronous transfer mode (ATM) based highperformance distributed computing (HPDC) environments. NCS capitalizes on thread based programming model to overlap computations and communications, and develop a dynamic message passing environment with separate data and control paths. This leads to a flexible and adaptive message passing environment that can support multiple flow control, error control, and multicasting algorithms. We provide an overview of the NCS architecture and present how NCS point to point communication services are implemented. We also analyze the overhead incurred by using multithreading and compare the performance of NCS point to point communication primitives with those of other message passing systems such as p4, PVM, and MPI. Benchmarking results indicate that NCS shows comparable performance to other systems for small message sizes but outperforms other systems for large message sizes.
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