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Journal Article
Revisiting the Regression between Raw Outputs of Image Quality Metrics and Ground Truth Measurements
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- Authors
- Chanho JUNG, Sanghyun JOO, Do-Won NAM, Wonjun KIM
- Issue Date
- 2016-11
- Citation
- IEICE Transactions on Information and Systems, v.E99.D, no.11, pp.2778-2787
- ISSN
- 1745-1361
- Publisher
- 일본, 전자정보통신학회 (IEICE)
- Language
- English
- Type
- Journal Article
- Abstract
- In this paper, we aim to investigate the potential usefulness of machine learning in image quality assessment (IQA).Most previous studies have focused on designing effective image quality metrics (IQMs), and significant advances have been made in the development of IQMs over the last decade. Here, our goal is to improve prediction outcomes of "any" given image quality metric. We call this the "IQM's Outcome Improvement" problem, in order to distinguish the proposed approach from the existing IQA approaches. We propose a method that focuses on the underlying IQM and improves its prediction results by using machine learning techniques. Extensive experiments have been conducted on three different publicly available image databases. Particularly, through both 1) indatabase and 2) cross-database validations, the generality and technological feasibility (in real-world applications) of our machine-learning-based algorithm have been evaluated. Our results demonstrate that the proposed framework improves prediction outcomes of various existing commonly used IQMs (e.g., MSE, PSNR, SSIM-based IQMs, etc.) in terms of not only prediction accuracy, but also prediction monotonicity.
- KSP Keywords
- Cross-database, Ground Truth, Learning-based, Machine Learning technique(MLT), Prediction accuracy, Real-world applications, Technological feasibility, image databases, image quality assessment(IQA), image quality metric, prediction monotonicity