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Color accuracy is an important but ambiguous image quality factor. Many viewers prefer enhanced color saturation; the most accurate color isn't necessarily the most pleasing. Nevertheless, it is important to measure a camera's color response: its color shifts, saturation, and the effectiveness of its white balance algorithms.
Accuracy is also used as a statistical measure of how well a binary classification test correctly identifies or excludes a condition. That is, the accuracy is the proportion of correct predictions (both true positives and true negatives) among the total number of cases examined. [10]
SNR is sometimes quantified in decibels (dB) of signal power relative to noise power, though in the imaging field the concept of "power" is sometimes taken to be the power of a voltage signal proportional to optical power; so a 20 dB SNR may mean either 10:1 or 100:1 optical power, depending on which definition is in use.
Sound quality is typically an assessment of the accuracy, fidelity, or intelligibility of audio output from an electronic device. Quality can be measured objectively, such as when tools are used to gauge the accuracy with which the device reproduces an original sound; or it can be measured subjectively, such as when human listeners respond to ...
The accuracy of tempo within an auditory image usually suffers when recalled; however, the consistency of a person's perception of tempo is preserved. When surveying subject's auditory imagery, their sense of tempo usually stays within 8% of the original tempo heard in a song that the subject heard at some point in the past. [1]
Video of air pollution data from Beijing being conveyed as a piece of music. Sonification is the use of non-speech audio to convey information or perceptualize data. [1] Auditory perception has advantages in temporal, spatial, amplitude, and frequency resolution that open possibilities as an alternative or complement to visualization techniques.
One definition of signal-to-noise ratio is the ratio of the power of a signal (meaningful input) to the power of background noise (meaningless or unwanted input): S N R = P s i g n a l P n o i s e , {\displaystyle \mathrm {SNR} ={\frac {P_{\mathrm {signal} }}{P_{\mathrm {noise} }}},}
In a classification task, the precision for a class is the number of true positives (i.e. the number of items correctly labelled as belonging to the positive class) divided by the total number of elements labelled as belonging to the positive class (i.e. the sum of true positives and false positives, which are items incorrectly labelled as belonging to the class).