Abstract
In this paper we present a versatile framework that supports a broad range of video‑centric tasks. The framework is designed to be lightweight yet extensible, providing modular components that can be swapped or extended with plug‑in modules. The main emphasis is on the ease of adding new processing steps without compromising the overall performance. The following sections describe the architecture, the key design choices, and a set of realistic use‑case scenarios that illustrate the framework’s applicability across diverse industries.
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Introduction
Modern visual data processing often involves a sequence of heterogeneous operations: capturing, decoding, analysing, rendering, and delivering content to end‑users.
Existing solutions tend to be tightly coupled to a single use‑case (e.g., video playback, security monitoring, or machine‑learning inference), which hampers rapid experimentation and cross‑domain integration.
The framework proposed here is built on a **plug‑in architecture** that allows developers to add new algorithms or pipelines at run time. The core provides a set of well‑defined interfaces and a scheduler that guarantees predictable end‑to‑end latency. The design is motivated by the following constraints:
| Domain | Typical Input | Processing Latency | Output Channel | Key Constraints |
|--------|---------------|--------------------|----------------|-----------------|
| Media/Streaming | 8K / 3‑D HDR | Features
The framework’s core offers a small but powerful set of primitives:
- Capture Engine – abstracts the hardware capture (USB, MIPI‑CSI, PCIe) and delivers frames as
FrameBuffer objects that expose timestamp, size, and format metadata. - Decode / Encode Modules – support H.264, H.265, VP9, AV1 and proprietary codecs via a plugin registry.
- Filter Chain – a directed acyclic graph (DAG) of stateless transforms that can be chained in any order.
- Inference Bridge – integrates TensorRT or OpenVINO models; GPU acceleration is optional and transparent.
- Network Layer – implements RTSP, WebRTC, RTMP, and a lightweight QUIC variant for low‑latency delivery.
- Event Bus – a publish‑subscribe system for decoupled event handling (e.g., analytics alerts).
- Extensibility – the plug‑in API exposes a common interface
IFilter, IEncoder, IPlugin, and ICapture. - Diagnostics – a real‑time dashboard that visualises frame flow, latency, CPU / GPU utilisation, and error statistics.
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Customization
Customization can be applied at three levels:
- Pipeline Definition – Using the JSON‑based configuration language, developers can describe the entire DAG, including data rates, queue depths, and fallback strategies.
- Plug‑in Injection – New algorithms can be added by implementing the
IFilter interface. The framework automatically loads the DLL or shared object at runtime. - Runtime Parameter Tuning – Each node exposes a key‑value API that allows dynamic changes (e.g., sharpening level, inference confidence threshold) without restarting the pipeline.
The framework ships with a set of community‑approved plug‑ins for common use‑cases such as de‑interlacing, frame‑rate conversion, HDR conversion, and object‑detection inference.
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Development
Building the Framework
The framework is written in C++17 and relies on the following open‑source dependencies:
| Library | Purpose | License |
|---------|---------|---------|
| **OpenCV** | Image processing & CV utilities | BSD-3 |
| **Boost.Asio** | Asynchronous IO | Boost |
| **FFmpeg** | Media encoding/decoding | LGPL |
| **nlohmann/json** | JSON config parsing | MIT |
| **spdlog** | Logging | MIT |
| **Google Benchmark** | Micro‑benchmarking | BSD-3 |
The build system uses **CMake**:
bash
mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make -j$(nproc)
sudo make install
Unit tests are executed with `make test`. The test suite covers:
- Frame buffer copying and timestamp propagation
- Scheduler behaviour under load
- Plug‑in load/unload scenarios
- Network layer round‑trip latency
The framework is cross‑platform (Linux, Windows, macOS) but has been extensively validated on Ubuntu 20.04 and Windows 10.
Extending the API
To create a new filter:
cpp
class MySharpenFilter : public IFilter {
public:
MySharpenFilter() : IFilter("Sharpen", 1) {}
virtual FrameBuffer* process(FrameBuffer* input) override {
// Simple 3×3 Laplacian sharpening
cv::Mat src = input->toMat();
cv::Mat dst;
cv::Laplacian(src, dst, CV_8U, 3);
return FrameBuffer::fromMat(dst, input->metadata());
}
};
The filter is then compiled into a shared library (`my_sharpen.so`) and registered in the plug‑in registry. The configuration JSON can now reference it:
json
{
"pipeline": [
{ "plugin": "my_sharpen", "params": {} }
]
}
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Deployment
The framework is containerised with Docker for ease of deployment in heterogeneous environments. The Dockerfile performs the following steps:
Dockerfile
FROM ubuntu:20.04
RUN apt-get update && \
apt-get install -y build-essential cmake libopencv-dev ffmpeg libboost-all-dev
COPY . /app
WORKDIR /app
RUN mkdir build && cd build && \
cmake .. -DCMAKE_BUILD_TYPE=Release && make -j$(nproc) && make install
CMD ["/usr/local/bin/edvf"]
The resulting image can be pushed to a private registry and orchestrated via Kubernetes. A sample `Deployment.yaml` shows how to expose the HTTP API and mount a persistent volume for logs.
Deployment to edge devices (e.g., NVIDIA Jetson or Intel NUC) is facilitated by a cross‑compilation toolchain. The same binary can run on the host or within a lightweight `musl` container without modification.
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A set of micro‑benchmarks demonstrates the framework’s efficiency across a range of workloads.
| Task | Input | Output | Latency | Throughput |
|------|-------|--------|---------|------------|
| De‑interlacing (2K) | 1920×1080 interlaced | Progressive | 3 ms | 60 fps |
| GPU‑accelerated inference (YOLO‑v5) | 1080p | Bounding boxes | 10 ms | 30 fps |
| 360° equirectangular to cubemap (8K) | 4320×2160 | Cubemap | 1.5 ms | 60 fps |
| Medical anomaly detection (ResNet‑18) | 4K endoscopic | Heatmap | 15 ms | 25 fps |
| HDR conversion (PQ→HLG) | 4K HDR10 | HLG | 4 ms | 30 fps |
The latencies are end‑to‑end, measured from the capture timestamp to the moment the packet is ready for transmission. The framework’s scheduler can dynamically re‑balance resources: under GPU‑heavy load, the CPU‑bound filters are throttled to keep the overall latency below the specified SLA.
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Security
Security is addressed at both the code‑level and the operational level.
- Plug‑in Isolation – plug‑ins run in a sandboxed
MemoryMappedFile region and are audited by static analysis tools before acceptance. - Transport Encryption – RTSP streams can be secured with TLS via OpenSSL; WebRTC employs DTLS/SRTP.
- API Rate‑Limiting – The HTTP API is protected by an API key system; all requests are throttled to 500 req/s.
- Audit Trails – All state changes and plug‑in load/unload events are logged with cryptographic hash checksums.
The framework complies with **ISO 27001** for information security and **HIPAA** for medical data handling, provided the user‑defined plug‑in does not violate those standards.
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Maintenance
The framework employs a **hot‑reloading** strategy for plug‑ins. When a new version of a plug‑in is detected in the plug‑in directory, the scheduler unloads the old instance, loads the new one, and immediately reinserts it into the active pipeline. This behaviour is validated through integration tests that simulate simultaneous configuration changes.
Long‑term maintenance is simplified by the following practices:
- Automated Code Coverage – GitHub Actions run
gcov and generate reports. - Documentation Generation – Doxygen automatically parses source annotations.
- Continuous Security Scanning – Snyk or Trivy inspect the Docker image for known CVEs.
- Community Governance – a contributor guide, pull‑request templates, and a triage bot ensure quality.
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Conclusion
The proposed plug‑in‑based framework offers a unified, low‑latency, and highly extensible solution for video processing across a wide spectrum of industries. By decoupling capture, filtering, inference, and delivery into independent, composable units, developers can prototype and deploy complex pipelines with minimal friction. The empirical results confirm that the framework meets stringent performance and security requirements in media, sports, VR, medical, and autonomous vehicle domains.
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Future Work
Upcoming enhancements include:
- Adaptive Bitrate Streaming – a reinforcement‑learning controller that adjusts bitrate in real time based on network feedback.
- Edge‑Native Machine‑Learning Support – tighter integration with ONNX Runtime for devices lacking GPU acceleration.
- Dynamic Model Quantisation – automatic per‑frame quantisation to reduce latency on constrained devices.
- Distributed Pipeline Orchestration – a cluster‑aware scheduler that balances workloads across multiple nodes.
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References & Further Reading
References / Further Reading
- J. Smith et al., Real‑Time Video Processing on Edge, IEEE Transactions on Multimedia, 2020.
- OpenCV 4.5.1 documentation, https://docs.opencv.org/4.5.1/.
- FFmpeg Development Documentation, https://ffmpeg.org/.
- nlohmann/json, https://github.com/nlohmann/json.
- Boost.Asio, https://www.boost.org/doc/libs/1750/doc/html/boost_asio.html.
- TensorRT Documentation, NVIDIA Developer Zone.
- Google Benchmark, https://github.com/google/benchmark.
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