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Distortion Classification in Computer Vision Applications: Current Progress, Challenges, and Perspectives
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AI
DOI：10.1145/3773023.png)
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Abstract
En 中文
Videos and images undergo various distortions and artefacts during acquisition, processing, compression, transmission, and storage. These distortions and artefacts may impact the performance of high-level Computer Vision (CV) tasks. Therefore, identifying distortions/artefacts is crucial for developing and improving CV technologies. This article provides an overview of the distortions and artefacts that affect the quality of image and video signals. It also offers a framework highlighting the role of databases, especially in this era where data represents the fuel of current technologies. Various methods related to CV and image processing, focusing on distortion detection and classification in images/videos, are discussed. A wide range of applications and techniques developed in computer vision and associated databases are also analysed. Our study finds that while Machine Learning (ML) based approaches provide promising results for single distortion, multiple distortion classification remains challenging. Moreover, some distortions are still missing in the existing datasets, indicating room for constructing more databases with various distortions necessary to improve the ML-based models used in various CV-based applications. This article benefits researchers working in CV applications where visual data quality plays an important role. It provides insights for designing efficient ML-based solutions for several real-world CV applications.
Keywords:
Surveillance videos
distortion detection
distortion classification
machine learning
deep learning
convolutional neural networks (CNN)
learning algorithms
Journal
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2.4K
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