The Helmholtz Method: Using Perceptual Compression to Reduce Machine Learning Complexity

Understand

This paper proposes a fundamental answer to a frequently asked question in multimedia computing and machine learning: Do artifacts from perceptual compression contribute to error in the machine learning process and if so, how much? Our approach to the problem is a reinterpretation of the Helmholtz Free Energy formula from physics to explain the relationship between content and noise when using sensors (such as cameras or microphones) to capture multimedia data.

• The reinterpretation allows a bit-measurement of the noise contained in images, audio, and video by combining a classifier with perceptual compression, such as JPEG or MP3.
• Our experiments on CIFAR-10 as well as Fraunhofer's IDMT-SMT-Audio-Effects dataset indicate that, at the right quality level, perceptual compression is actually not harmful but contributes to a significant reduction of complexity of the machine learning process.
• That is, our noise quantification method can be used to speed up the training of deep learning classifiers significantly while maintaining, or sometimes even improving, overall classification accuracy.