Observer-Usable Information as a Task-specific Image Quality Metric
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917358719729664 |
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| author | Lu, Changjie Sengupta, Sourya Li, Hua Anastasio, Mark A. |
| author_facet | Lu, Changjie Sengupta, Sourya Li, Hua Anastasio, Mark A. |
| contents | Objective, task-based measures of image quality (IQ) have been widely advocated for assessing and optimizing medical imaging technologies. Besides signal detection theory-based measures, information-theoretic quantities have been proposed to quantify task-based IQ. For example, task-specific information (TSI), defined as the mutual information between an image and a task variable, represents an optimal measure of how informative an image is for performing a specified task. However, like the ideal observer from signal detection theory, TSI does not quantify the amount of task-relevant information in an image that can be exploited by a sub-ideal observer. A recently proposed relaxation of TSI, termed predictive V-information (V-info), removes this limitation and can quantify the utility of an image with consideration of a specified family of sub-ideal observers. In this study, for the first time, we introduce and investigate V-info as an objective, task-specific IQ metric. To corroborate its usefulness, a stylized magnetic resonance image restoration problem is considered in which V-info is employed to quantify signal detection or discrimination performance. The presented results show that V-info correlates with area under the receiver operating characteristic (ROC) curve for binary tasks, while being readily applicable to multi-class (>2) tasks where ROC analysis is challenging. Notably, V-info exhibits greater sensitivity in scenarios where conventional metrics saturate. These findings demonstrate that V-info represents a new objective IQ measure that can complement conventional signal detection theory-based ones. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_00298 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Observer-Usable Information as a Task-specific Image Quality Metric Lu, Changjie Sengupta, Sourya Li, Hua Anastasio, Mark A. Image and Video Processing Objective, task-based measures of image quality (IQ) have been widely advocated for assessing and optimizing medical imaging technologies. Besides signal detection theory-based measures, information-theoretic quantities have been proposed to quantify task-based IQ. For example, task-specific information (TSI), defined as the mutual information between an image and a task variable, represents an optimal measure of how informative an image is for performing a specified task. However, like the ideal observer from signal detection theory, TSI does not quantify the amount of task-relevant information in an image that can be exploited by a sub-ideal observer. A recently proposed relaxation of TSI, termed predictive V-information (V-info), removes this limitation and can quantify the utility of an image with consideration of a specified family of sub-ideal observers. In this study, for the first time, we introduce and investigate V-info as an objective, task-specific IQ metric. To corroborate its usefulness, a stylized magnetic resonance image restoration problem is considered in which V-info is employed to quantify signal detection or discrimination performance. The presented results show that V-info correlates with area under the receiver operating characteristic (ROC) curve for binary tasks, while being readily applicable to multi-class (>2) tasks where ROC analysis is challenging. Notably, V-info exhibits greater sensitivity in scenarios where conventional metrics saturate. These findings demonstrate that V-info represents a new objective IQ measure that can complement conventional signal detection theory-based ones. |
| title | Observer-Usable Information as a Task-specific Image Quality Metric |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2510.00298 |