Deep Convolutional Neural Models for Picture-Quality Prediction: Challenges and Solutions to Data-Driven Image Quality Assessment
Abstract
Convolutional neural networks (CNNs) have been shown to deliver standout performance on a wide variety of visual information processing applications. However, this rapidly developing technology has only recently been applied with systematic energy to the problem of picture-quality prediction, primarily because of limitations imposed by a lack of adequate ground-truth human subjective data. This situation has begun to change with the development of promising data-gathering methods that are driving new approaches to deep-learning-based perceptual picture-quality prediction. Here, we assay progress in this rapidly evolving field, focusing, in particular, on new ways to collect large quantities of ground-truth data and on recent CNN-based picture-quality prediction models that deliver excellent results in a large, real-world, picture-quality database.
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Transfer learning is a promising technique for the blind UGC-VQA problem.
“This suggests that transfer learning is a promising technique for the blind UGC-VQA problem, consistent with conclusions drawn for picture-quality prediction [63].”
From UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content (2021) · cites this paper as [63]
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- Authors
- Kim, J., Zeng, H., Ghadiyaram, D., Lee, S., Zhang, L., & Bovik, A. C.
- Affiliations
- Yonsei University (KR)
- Hong Kong Polytechnic University (HK)
- The University of Texas at Austin (US)
- Venue
- IEEE Signal Processing Magazine
- Published
- 2017-11
- Volume
- 34
- Issue
- 6
- Pages
- 130-141
- ISSN
- 1053-5888 (ISSN-L)
- 1558-0792
- OpenAlex
- W2767836774
- Cited by
- 283
- References
- 34
- Citations by year
- 2026: 7 · 2025: 22 · 2024: 12 · 2023: 34 · 2022: 33 · 2021: 46 · 2020: 46 · 2019: 46 · 2018: 33 · 2017: 4
- FWCI
- 14.64
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Signal Processing Magazine (publishedVersion)
- PolyU Institutional Research Archive (Hong Kong Polytechnic University) (submittedVersion)
- Funders
- National Research Foundation of Korea (NRF)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- image quality assessment, convolutional neural networks, perceptual image quality, ground truth data collection, image quality database
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