@inproceedings{d1fecf59b88f44499fd0acc92c9dd430,
title = "RHDDNet: Multi-label Classification-Based Detection of Image Hybrid Distortions",
abstract = "Image distortion detection is a key step in image quality assessment and image reconstruction algorithms. In previous work, a large number of research focus on detecting the single distortion in the image. However, the number of distortion types in the image is often uncertain. Thus, we propose a model that can be used for hybrid distortion detection. Concretely, we transform the hybrid distortion detection task into a multi-label classification task and abstract it as a convolutional network optimization problem. A dataset is created to train the model and evaluate its performance. Experiments show that the proposed model performs well in the detection of hybrid distortions in images.",
keywords = "Image distortion detection, deep learning, multi-label classification, residual network",
author = "Bowen Dou and Hai Li and Shujuan Hou",
note = "Publisher Copyright: {\textcopyright} 2022 SPIE.; 14th International Conference on Digital Image Processing, ICDIP 2022 ; Conference date: 20-05-2022 Through 23-05-2022",
year = "2022",
doi = "10.1117/12.2643514",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Xudong Jiang and Wenbing Tao and Deze Zeng and Yi Xie",
booktitle = "Fourteenth International Conference on Digital Image Processing, ICDIP 2022",
address = "United States",
}