Noise-cuts-Noise Approach for Mitigating the JPEG Distortions in Deep Learning
Ijaz Ahmad, Seokjoo Shin · 2023
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.