Image Processing Benchmark Suite

A high-performance benchmarking framework for evaluating image processing algorithms, libraries, and pipelines. The suite measures execution time, throughput, CPU utilization, memory usage, and scalability under different workloads to help analyze and optimize image processing performance.

Designed for performance engineers, computer vision engineers, systems programmers, and AI infrastructure developers.

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Features

• Benchmark common image processing operations
• Compare multiple image processing libraries
• Measure execution time and throughput
• CPU utilization monitoring
• Memory usage analysis
• Multi-threaded benchmarking
• Batch image processing benchmarks
• Configurable benchmark parameters
• JSON, CSV, and HTML report generation
• Reproducible benchmarking environment

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Supported Operations

• Image Loading
• Image Saving
• Resize
• Crop
• Rotate
• Flip
• Color Space Conversion
• Grayscale Conversion
• Gaussian Blur
• Median Blur
• Bilateral Filter
• Edge Detection
• Histogram Equalization
• Thresholding
• Morphological Operations
• Image Compression

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Performance Metrics

The framework measures:

• Execution Time
• Average Latency
• P95 Latency
• P99 Latency
• Images per Second
• Throughput
• CPU Usage
• Memory Usage
• Peak Memory Consumption
• Batch Processing Performance
• Multi-thread Scaling Efficiency

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Benchmark Scenarios

• Single Image Processing
• Batch Image Processing
• Large Image Benchmark
• Small Image Benchmark
• High Resolution Images
• Parallel Processing
• Multi-threaded Processing
• Library Comparison
• Algorithm Comparison
• Stress Testing

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Supported Libraries

Examples include:

• OpenCV
• Pillow
• ImageMagick
• libvips

Additional image processing libraries can be added for comparison.

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Example Benchmark Output

Benchmark: Image Resize

Input Resolution: 3840x2160
Output Resolution: 1920x1080

Average Time:      4.81 ms
Images/sec:        208
CPU Usage:         47%
Memory Usage:      186 MB
Peak Memory:       241 MB

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Project Structure

Image-Processing-Benchmark-Suite/

├── cmd/
├── internal/
│   ├── benchmark/
│   ├── algorithms/
│   ├── metrics/
│   ├── reports/
│   └── utils/
├── datasets/
├── configs/
├── examples/
├── reports/
├── docs/
├── scripts/
└── tests/

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Roadmap

Phase 1

• Benchmark framework
• Core image operations
• Metrics collection

Phase 2

• Multi-threaded execution
• Library comparison
• Report generation

Phase 3

• Interactive dashboard
• Batch benchmarking
• Automated performance comparison

Phase 4

• GPU benchmarking
• SIMD optimization benchmarks
• Distributed benchmarking
• Continuous performance regression testing

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Tech Stack

• Go
• OpenCV
• Python
• C++
• Performance Profiling
• Benchmarking
• JSON
• CSV

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Target Users

• Performance Engineers
• Computer Vision Engineers
• AI Infrastructure Engineers
• Backend Engineers
• Systems Engineers
• ML Engineers
• Open Source Contributors

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Future Enhancements

• GPU acceleration benchmarks
• CUDA support
• OpenCL benchmarking
• SIMD performance analysis
• Real-time dashboard
• Prometheus integration
• Grafana dashboards
• CI performance regression testing

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Contributing

Contributions are welcome. Feel free to submit issues, feature requests, benchmark scenarios, or pull requests.

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License

This project is licensed under the MIT License.