No abstract
Most modern computers are equipped with powerful yet cost-effective Graphics Processing Units (GPUs) to accelerate graphics operations. Although programmable shaders on these GPUs were designed for the creation of 3-D rendering effects, they can also be used as generic processing units for vector data. This paper proposes a hardware renderer capable of executing motion compensation, reconstruction, and visualization entirely on the GPU by the use of vertex and pixel shaders. Our measurements show that a speedup of 297% can be achieved by relying on the processing power of the GPU, relative to the CPU. As an example, real-time playback of high-definition video (1080p) was achieved at 62.0 frames per second, consuming only 68.2% of all CPU cycles on a modern machine.
Abstract. News production is characterized by a complex and dynamic workflow, in which it is important to produce and broadcast reliable news as fast as possible. In this process, the efficient retrieval of previously broadcasted news items is important, both for gathering background information and for reuse of footage in new reports. This paper discusses how the quality of descriptive metadata of news items can be optimized, by collecting data generated during news production. Starting from a description of the news production process of the Flemish public service broadcaster in Belgium (VRT), information systems containing valuable metadata are identified. Subsequently, we present a data model that uniformly represents the available information generated during news production. This data model is then implemented using Semantic Web technologies. Further, we describe how other valuable data sets, present in the Semantic Web, are connected to the data model, enabling semantic search operations.
The coding efficiency of the H.264/AVC standard makes the decoding process computationally demanding. This has limited the availability of cost-effective, high-performance solutions. Modern computers are typically equipped with powerful yet cost-effective Graphics Processing Units (GPUs) to accelerate graphics operations. These GPUs can be addressed by means of a 3-D graphics API such as Microsoft Direct3D or OpenGL, using programmable shaders as generic processing units for vector data. The new CUDA (Compute Unified Device Architecture) platform of NVIDIA provides a straightforward way to address the GPU directly, without the need for a 3-D graphics API in the middle. In CUDA, a compiler generates executable code from C code with specific modifiers that determine the execution model. This paper first presents an own-developed H.264/AVC renderer, which is capable of executing motion compensation (MC), reconstruction, and Color Space Conversion (CSC) entirely on the GPU. To steer the GPU, Direct3D combined with programmable pixel and vertex shaders is used. Next, we also present a GPU-enabled decoder utilizing the new CUDA architecture from NVIDIA. This decoder performs MC, reconstruction, and CSC on the GPU as well. Our results compare both GPU-enabled decoders, as well as a CPU-only decoder in terms of speed, complexity, and CPU requirements. Our measurements show that a significant speedup is possible, relative to a CPU-only solution. As an example, real-time playback of high-definition video (1080p) was achieved with our Direct3D and CUDA-based H.264/AVC renderers.
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