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".^: 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.^: 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}