![]() ![]() Subjectively: Requiring many observers and many presentations of a representative range of impairment conditions and content types. Subjective testing conditions must be closely controlled, with appropriate screening of observers and postprocessing of the results to ensure consistency and statistical significance. They are costly and time consuming, but generally effective. Objectively: Using metrics that attempt to capture the perceptual mechanisms of the human visual system (HVS). The main issue here is that simple metrics bear little relevance to the HVS and generally do not correlate well with subjective results, especially at lower bit rates when distortions are higher. More complex, perceptually inspired metrics, although improving significantly in recent years, can still be inconsistent under certain test conditions. The outcome of this is that mean squared error (MSE)-based metrics are still the most commonly used assessment methods, both for in-loop optimization and for external performance comparisons. Noise or artifacts are introduced into image and video content throughout the whole processing workflow. Noise can be defined from different perspectives: These include sensor noise introduced during content acquisition, artifacts caused by compression during coding, transmission errors due to noisy communication channels, and sample aliasing due to resolution adaptation (if applied). Mathematical distortion: An objective measure of difference between the original acquired content and the processed version. Perceptual quality: A measure of the perceptual impact of the unwanted signal and how it detracts from the enjoyment or interpretation of an image or video.
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