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Characteristic of an image From Wikipedia, the free encyclopedia
Image quality can refer to the level of accuracy with 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".[1]: 598 The difference between the two definitions is that one focuses 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.[2]: 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.[3] 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]: 2 [3]: 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 distortions 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.
Image quality can be assessed using objective or subjective methods. In the objective method, image quality assessments are performed by different algorithms that analyze the distortions and degradations introduced in an image. Subjective image quality assessments are a method based on the way in which humans experience or perceive image quality. Objective and subjective methods of quality assessment don't necessarily correlate with each other. An algorithm might have a similar value for an image and its altered or degraded versions, while a subjective method might perceive a stark contrast in quality for the same image and its versions.
Subjective methods for image quality assessment belong to the larger area of psychophysics research, a field that studies the relationship between physical stimulus and human perceptions. A subjective IQA method will typically consist on applying mean opinion score techniques, where a number of viewers rate their opinions based on their perceptions of image quality. These opinions are afterwards mapped onto numerical values.
These methods can be classified depending on the availability of the source and test images:
Since visual perception can be affected by environmental and viewing conditions, the International Telecommunication Union produced a set of recommendations for standardized testing methods for subjective image quality assessment.[4]
Wang & Bovik (2006) classify the objective methods with the following criteria: (a) the availability of an original image; (b) on the basis of their application scopes and (c) on the model of a Human Visual System simulation to assess quality.[5] Keelan (2002) classifies the methods based on (a) direct experimental measurements; (b) system modeling and (c) visual assessment against calibrated standards.[6]: 173
Image quality metrics can also be classified in terms of measuring only one specific type of degradation (e.g., blurring, blocking, or ringing), or taking into account all possible signal distortions, that is, multiple kinds of artifacts.[7]
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