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Balancing the Encoder and Decoder Complexity in Image Compression for Classification.

Zhihao Duan1, Adnan Faisal Hossain1, Jiangpeng He1

  • 1Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, 47907, IN, U.S.A.

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Summary
This summary is machine-generated.

This study explores image coding for machine classification, finding a trade-off between encoder and decoder sizes. A new feature compression method offers flexible rate-accuracy control for efficient image classification.

Keywords:
Coding for machineslearned image compressionrate-accuracy-complexity

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Data Compression

Background:

  • Analyzing the computational complexity of image coding for machine classification is crucial for optimizing performance.
  • Understanding the rate-accuracy trade-off in compression for classification is essential for efficient model design.

Approach:

  • Empirical investigation of encoder and decoder sizes to reveal their complementary relationship in image classification.
  • Introduction of a novel feature compression-based method for efficient image compression tailored for classification tasks.
  • Implementation of adjustable rate, accuracy, and model size using a single feature compression framework within neural networks.

Key Points:

  • Demonstrated a complementary relationship: a large encoder pairs well with a small decoder, and vice versa.
  • Developed a flexible method allowing adjustable compression rates, classification accuracy, and model component sizes.
  • Achieved competitive results on ImageNet classification, showcasing flexibility and efficiency.

Conclusions:

  • The proposed feature compression method provides a versatile approach to image compression for classification.
  • This work offers a new perspective on balancing encoder-decoder complexity for improved rate-accuracy performance.
  • The study highlights the potential for adaptable compression strategies in deep learning-based image classification.