Image Distortion Detection using Convolutional Neural Network
This work addresses image distortion detection for applications like image compression and restoration, but it appears incremental as it builds on existing methods.
The paper tackles the problem of detecting distortion regions and classifying distortion types in images, achieving significant outperformance over the state-of-the-art distortion classifier and reporting accurate detection results for the first time.
Image distortion classification and detection is an important task in many applications. For example when compressing images, if we know the exact location of the distortion, then it is possible to re-compress images by adjusting the local compression level dynamically. In this paper, we address the problem of detecting the distortion region and classifying the distortion type of a given image. We show that our model significantly outperforms the state-of-the-art distortion classifier, and report accurate detection results for the first time. We expect that such results prove the usefulness of our approach in many potential applications such as image compression or distortion restoration.