Noise-cuts-Noise Approach for Mitigating the JPEG Distortions in Deep Learning

Abstract

Lossy image compression provides an efficient solution to the exchange and storage of large volumes of image data for various applications. The main design principle of a lossy compression algorithm is to discard visually insignificant information as much as possible while keeping the resulted visible artifacts at a minimum. However, these unperceivable defects significantly degrade the performance of a trained deep learning (DL) model. Therefore, to improve the classification performance of the models on noisy images, we propose a noise-based data augmentation technique called noise-cuts-noise approach. The simulation analysis have shown that the proposed method efficiently mitigates the performance gap on highly compressed images for example, the accuracy difference is reduced from 11% to 2% for classification of natural images. For uncompressed images, the model performance is either preserved or improved. In addition, to validate the usefulness of the proposed method, we considered a case study of multi-label classification task in chest X-ray (CXR) images. The model accuracy on highly compressed images with the proposed augmentation method increased 2% on higher resolution images while the accuracy difference reduced from 6% to 1% on smaller resolution images.