Video quality metrics are algorithms designed to predict how actual viewers would gauge video quality. These metrics are used for a range of activities, from comparing codecs and different encoding configurations, to assisting in production and live quality of experience (QoE) monitoring. Image quality can degrade due to distortions during image acquisition and processing. Examples of distortion include noise, blurring, ringing, and compression artifacts. Efforts have been made to create objective measures of quality. For many applications, a valuable quality metric correlates well with the subjective perception of quality by a human observer. Quality metrics can also track unperceived errors as they propagate through an image processing pipeline, and can be used to compare image processing algorithms. If an image without distortion is available, you can use it as a reference to measure the quality of other images. For example, when evaluating the quality of compressed images, an uncompressed version of the image provides a useful reference. In these cases, you can use full-reference quality metrics to directly compare the target image and the reference image. If a reference image without distortion is not available. you can use a no-reference image quality metric instead. These metrics compute quality scores based on expected image statistics.
Full-Reference Quality Metrics Full-reference algorithms compare the input image against a pristine reference image with no distortion.
No-Reference Quality Metrics No-reference algorithms use statistical features of the input image to evaluate the image quality.