Information

Abstract

This thesis investigates GPU-based video processing in the context of a graphics system for live TV broadcasting. Upcoming TV standards like UHD-1 result in much higher data rates than existing formats. Processing such data rates while satisfying the real-time requirement of live TV poses a particular challenge for the implementation of a software-based broadcast graphics system. In order to reach the required data rates, the software needs to process image data concurrently on the central processing unit (CPU) and graphics processing unit (GPU) of the machine. In particular, the transfers of image data between main and graphics memory need to be overlapped with CPU-based and GPU-based executions in order to maximize data throughput. In this thesis, we therefore investigate the following questions: Which methods are available to a software implementation in order to reach this level of parallelism? Which data rates can actually be reached using these methods? In order to answer these questions, we implement a prototype of a software for rendering TV graphics. To take advantage of the GPU’s ability to efficiently process image data, we use the OpenGL application programming interface (API). We use advanced methods of OpenGL programming to render high-quality video and increase the level of employed parallelism of the GPU. We implement the transcoding between RGB and the professional video format V210, which is more complex to process than conventional consumer-oriented image formats. In our software, we apply the pipeline programming pattern in order to distribute stages of the video processing algorithm to different threads. As a result, those stages execute concurrently on different hardware units of the system. Our prototype exposes the applied degree of concurrency to the user as a collection of different optimization settings. In order to evaluate these optimizations, we integrate a profiling mechanism directly into the execution of the pipeline. This allows us to automatically create performance profiles while running our prototype with various test scenarios. The results of this thesis are based on the analysis of these traces. Our prototype shows that the methods described in this thesis enable a software program to process high-resolution video in high quality. The results of our evaluations also show that there is no single best optimization setting for every GPU architecture. Different driver implementations and hardware features require our prototype to apply different optimization settings for each device. The ability of our software structure to dynamically change the degree of concurrency is therefore an important feature. For broadcasting software that is expected to perform well on a range of hardware devices, this is ultimately an essential feature.

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BibTeX

@mastersthesis{fink-2013-gvp,
  title =      "GPU-based Video Processing in the Context of TV Broadcasting",
  author =     "Heinrich Fink",
  year =       "2013",
  abstract =   "This thesis investigates GPU-based video processing in the
               context of a graphics system for live TV broadcasting.
               Upcoming TV standards like UHD-1 result in much higher data
               rates than existing formats. Processing such data rates
               while satisfying the real-time requirement of live TV poses
               a particular challenge for the implementation of a
               software-based broadcast graphics system. In order to reach
               the required data rates, the software needs to process image
               data concurrently on the central processing unit (CPU) and
               graphics processing unit (GPU) of the machine. In
               particular, the transfers of image data between main and
               graphics memory need to be overlapped with CPU-based and
               GPU-based executions in order to maximize data throughput.
               In this thesis, we therefore investigate the following
               questions: Which methods are available to a software
               implementation in order to reach this level of parallelism?
               Which data rates can actually be reached using these
               methods? In order to answer these questions, we implement a
               prototype of a software for rendering TV graphics. To take
               advantage of the GPU’s ability to efficiently process
               image data, we use the OpenGL application programming
               interface (API). We use advanced methods of OpenGL
               programming to render high-quality video and increase the
               level of employed parallelism of the GPU. We implement the
               transcoding between RGB and the professional video format
               V210, which is more complex to process than conventional
               consumer-oriented image formats. In our software, we apply
               the pipeline programming pattern in order to distribute
               stages of the video processing algorithm to different
               threads. As a result, those stages execute concurrently on
               different hardware units of the system. Our prototype
               exposes the applied degree of concurrency to the user as a
               collection of different optimization settings. In order to
               evaluate these optimizations, we integrate a profiling
               mechanism directly into the execution of the pipeline. This
               allows us to automatically create performance profiles while
               running our prototype with various test scenarios. The
               results of this thesis are based on the analysis of these
               traces. Our prototype shows that the methods described in
               this thesis enable a software program to process
               high-resolution video in high quality. The results of our
               evaluations also show that there is no single best
               optimization setting for every GPU architecture. Different
               driver implementations and hardware features require our
               prototype to apply different optimization settings for each
               device. The ability of our software structure to dynamically
               change the degree of concurrency is therefore an important
               feature. For broadcasting software that is expected to
               perform well on a range of hardware devices, this is
               ultimately an essential feature. ",
  month =      sep,
  address =    "Favoritenstrasse 9-11/186, A-1040 Vienna, Austria",
  school =     "Institute of Computer Graphics and Algorithms, Vienna
               University of Technology",
  keywords =   "transfer, rendering, video processing, concurrency, OpenGL",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2013/fink-2013-gvp/",
}