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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.
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 ...
Traditionally, SNR is defined to be the ratio of the average signal value to the standard deviation of the signal : [2] [3] = when the signal is an optical intensity, or as the square of this value if the signal and noise are viewed as amplitudes (field quantities).
Video quality is a characteristic of a video passed through a video transmission or processing system that describes perceived video degradation (typically compared to the original video). Video processing systems may introduce some amount of distortion or artifacts in the video signal that negatively impact the user's perception of the system.
The benefits of using digital recorders with greater than 16-bit accuracy can be applied to the 16 bits of audio CD. Meridian Audio founder John Robert Stuart stresses that with the correct dither , the resolution of a digital system is theoretically infinite, and that it is possible, for example, to resolve sounds at −110 dB (below digital ...
A measurement system can be accurate but not precise, precise but not accurate, neither, or both. For example, if an experiment contains a systematic error, then increasing the sample size generally increases precision but does not improve accuracy. The result would be a consistent yet inaccurate string of results from the flawed experiment.
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).
Typical values for the PSNR in lossy image and video compression are between 30 and 50 dB, provided the bit depth is 8 bits, where higher is better. The processing quality of 12-bit images is considered high when the PSNR value is 60 dB or higher. [3] [4] For 16-bit data typical values for the PSNR are between 60 and 80 dB.