In recent years, as the application of large-screen display technology has deepened across various fields, the market has moved beyond simple single-screen displays and now favors interactive, fused desktop environments. Most current desktop fusion systems rely on hardware like split screens, which are costly and inefficient. To address these limitations, this paper presents a video display system that leverages CUDA (Compute Unified Device Architecture), a parallel computing platform developed by NVIDIA. By utilizing high-performance GPUs, the system improves real-time processing of large-scale graphical data. The proposed system performs three key tasks: image segmentation, Bezier curve fitting, and image fusion, achieving a precision of 14 pixels with high real-time performance.
The core of image processing lies in large matrix operations, which are ideal for parallel computation. While CPUs struggle to take full advantage of this, GPUs excel in handling parallel tasks efficiently. In the CUDA model, the CPU acts as the host, while the GPU serves as a coprocessor, enabling highly threaded parallel processing. This architecture allows for efficient distribution of tasks across multiple devices.
The system is built on an NVIDIA GeForce GTX470 platform, using its multi-head output feature to connect multiple projectors into a seamless screen array without additional hardware. Given the high volume of image data and the need for real-time performance, the system employs both multi-CPU and CUDA-based parallel computing techniques. Each projection device is assigned a dedicated thread, linked to a specific CPU core and GPU compute unit, ensuring full parallelism and avoiding performance degradation as more devices are added.
A timer mechanism is used to collect and store control screen images periodically, then initiate threads for image segmentation, fusion, and display. These threads are associated with fixed GPU cores, and the CUDA framework handles the texture mapping and image processing. The Bezier curve method is applied for image fusion, where each line of the target image is fitted using control points. The Bezier basis function defines the curve, ensuring smooth transitions between control points.
Although OpenGL offers convenient functions for generating Bezier curves, it lacks hardware-level control and is not as efficient. Instead, the system uses CUDA’s parallel matrix operations to process textures efficiently. The Bezier calculation is distributed across GPU threads, allowing for coarse-grained parallelism and significantly improving real-time performance.
Performance evaluation shows that real-time display requires at least 25 frames per second, meaning the entire image processing must be completed within 40 milliseconds. Testing different GPU models, the GeForce GTX470 demonstrated excellent performance, capable of displaying 35–40 frames per second, providing a smooth user experience.
In conclusion, the demand for large-screen computer demonstrations continues to grow, and GPU computing has become a key solution for large-scale parallel tasks. This system uses CUDA to implement a video splicing solution, currently supporting up to four screens with two dual-head graphics cards. Future expansions could include network-based CS structures. Since GPU handles all time-consuming tasks, the system scales efficiently without performance loss. With its parallel design, the system can easily integrate human-computer interaction and realistic rendering modules, evolving into a high-performance multimedia display system that delivers an immersive user experience.
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