Physics Maths Engineering
Rui Zhu,
Fei Zhou,
Wenming Yang
Peer Reviewed
Image quality assessment is usually achieved by pooling local quality scores. However, commonly used pooling strategies, based on simple sample statistics, are not always sensitive to distortions. In this short communication, we propose a novel perspective of pooling: reliable pooling through statistical hypothesis testing, which enables effective detection of subtle changes of population parameters when the underlying distribution of local quality scores is affected by distortions. To illustrate the significance of this novel perspective, we design a new pooling strategy utilising simple one-sided one-sample t -test. The experiments on benchmark databases show the reliability of hypothesis testing-based pooling, compared with state-of-the-art pooling strategies.
Image quality assessment (IQA) is the process of evaluating the visual quality of an image, often by analyzing distortions like blur, noise, or compression artifacts. It is essential for applications like photography, medical imaging, and video streaming.
Pooling is the process of combining local quality scores (e.g., from different regions of an image) into a single overall quality score. Traditional pooling methods use simple statistics like averages, but they may not detect subtle distortions effectively.
The study introduces a novel pooling strategy based on statistical hypothesis testing, specifically using a one-sided, one-sample t-test. This method detects subtle changes in the distribution of local quality scores caused by distortions, making it more reliable than traditional pooling methods.
Hypothesis testing is more sensitive to subtle changes in the underlying distribution of local quality scores. Unlike simple statistics (e.g., mean or median), it can effectively detect distortions that might otherwise go unnoticed, improving the accuracy of image quality assessment.
The one-sample t-test compares the local quality scores to a reference value, determining whether the scores significantly deviate from expected quality levels. This helps identify distortions that affect the overall image quality.
The hypothesis testing-based pooling strategy:
The method was tested on benchmark image quality databases, comparing its performance to existing pooling strategies. Results showed that hypothesis testing-based pooling is more reliable and sensitive to distortions.
This method can be used in:
While deep learning methods require large datasets and computational resources, this hypothesis testing-based approach is simpler, more interpretable, and equally effective for detecting subtle distortions.
Future research could explore:
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Total | 451 | 451 |
Show by month | Manuscript | Video Summary |
---|---|---|
2025 April | 2 | 2 |
2025 March | 56 | 56 |
2025 February | 51 | 51 |
2025 January | 46 | 46 |
2024 December | 44 | 44 |
2024 November | 37 | 37 |
2024 October | 28 | 28 |
2024 September | 38 | 38 |
2024 August | 25 | 25 |
2024 July | 30 | 30 |
2024 June | 28 | 28 |
2024 May | 36 | 36 |
2024 April | 24 | 24 |
2024 March | 6 | 6 |
Total | 451 | 451 |