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Image quality

Image quality can refer to the level of accuracy in which different imaging systems capture, process, store, compress, transmit and display the signals that form an image. Another definition refers to image quality as 'the weighted combination of all of the visually significant attributes of an image'.:598 The difference between the two definitions is that one focus on the characteristics of signal processing in different imaging systems and the latter on the perceptual assessments that make an image pleasant for human viewers. Image quality can refer to the level of accuracy in which different imaging systems capture, process, store, compress, transmit and display the signals that form an image. Another definition refers to image quality as 'the weighted combination of all of the visually significant attributes of an image'.:598 The difference between the two definitions is that one focus on the characteristics of signal processing in different imaging systems and the latter on the perceptual assessments that make an image pleasant for human viewers. Image quality should not be mistaken with image fidelity. Image fidelity refers to the ability of a process to render a given copy in a perceptually similar way to the original (without distortion or information loss), i.e., through a digitization or conversion process from analog media to digital image. The process of determining the level of accuracy is called Image Quality Assessment (IQA). Image quality assessment is part of the quality of experience measures. Image quality can be assessed using two methods: subjective and objective. Subjective methods are based on the perceptual assessment of a human viewer about the attributes of an image or set of images, while objective methods are based on computational models that can predict perceptual image quality.:vii Objective and subjective methods aren't necessarily consistent or accurate between each other: a human viewer might perceive stark differences in quality in a set of images where a computer algorithm might not. Subjective methods are costly, require a large number of people, and are impossible to automate in real-time. Therefore, the goal of image quality assessment research is to design algorithms for objective assessment that are also consistent with subjective assessments. The development of such algorithms has a lot of potential applications. They can be used to monitor image quality in control quality systems, to benchmark image processing systems and algorithms and to optimize imaging systems.:2:430 The image formation process is affected by several distortions between the moment in which the signals travel through to and reach the capture surface, and the device or mean in which signals are displayed. Although optical aberrations can cause great distorsions in image quality, they are not part of the field of Image Quality Assessment. optical aberrations caused by lenses belong to the optics area and not to the signal processing areas. In an ideal model, there's no quality loss between the emission of the signal and the surface in which the signal is being captured on. For example, a digital image is formed by electromagnetic radiation or other waves as they pass through or reflect off objects. That information is then captured and converted into digital signals by an image sensor. The sensor, however, has nonidealities that limit its performance.

[ "Algorithm", "Computer vision", "Optics", "Artificial intelligence", "Image (mathematics)", "Suboptimal Image", "Cone beam reconstruction", "patient dose", "Detective quantum efficiency", "visual grading" ]
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