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First Gradually, Then Suddenly: Understanding the Impact of Image Compression on Object Detection Using Deep

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Image compression impacts object detection performance in video surveillance. This study shows how to find optimal Joint Photographic Expert Group (JPEG) compression levels for deep learning models, balancing quality and data storage.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Image Processing

Background:

  • Video surveillance systems generate vast amounts of image data.
  • Lossy compression, like Joint Photographic Expert Group (JPEG), is used for efficient storage and transfer.
  • However, JPEG compression can degrade image quality, potentially affecting subsequent analysis.

Purpose of the Study:

  • To investigate the impact of JPEG image compression on the performance of object detection models.
  • To analyze how varying compression levels affect the accuracy and robustness of deep learning-based object detectors.
  • To provide guidance for practitioners on selecting appropriate compression levels for surveillance applications.

Main Methods:

  • The study focused on Joint Photographic Expert Group (JPEG) compression.
  • Performance metrics were thoroughly analyzed across a range of compression characteristics.
  • Nine popular object-detection deep learning models were evaluated on a standard benchmark dataset.

Main Results:

  • The research experimentally assessed the robustness of nine object-detection deep models against different JPEG compression levels.
  • Significant variations in model performance were observed based on the degree of image compression.
  • The study identified a trade-off between compression ratio and object detection accuracy.

Conclusions:

  • Practitioners can use the findings to determine acceptable JPEG compression levels for specific video surveillance use cases.
  • This methodology aids in managing large image datasets by optimizing compression without critically compromising object detection.
  • The research contributes to efficient processing and long-term retention of surveillance imagery.