Unlike a panel of human observers, an objective model should always deterministically output the same quality score for a given set of input parameters. Objective quality models are sometimes also referred to as "instrumental (quality) models", in order to emphasize their application as measurement instruments. Some authors suggest that the term “objective” is misleading, as it “implies that instrumental measurements bear objectivity, which they only do in case that they can be generalized.” Objective models can be classified by the amount of information available about the original signal, the received signal, or whether there is a signal present at all:   Some models that are used for video quality assessment (such as PSNR or SSIM) are simply image quality models, whose output is calculated for every frame of a video sequence. This quality measure of every frame can then be recorded and pooled over time to assess the quality of an entire video sequence. While this method is easy to implement, it does not factor in certain kinds of degradations that develop over time, such as the moving artifacts caused by packet loss and its concealment. A video quality model that considers the temporal aspects of quality degradations, like VQM or the MOVIE Index, may be able to produce more accurate predictions of human-perceived quality. An overview of recent no-reference image quality models has been given in a journal paper by Shahid et al. As mentioned above, these can be used for video applications as well. No-reference, pixel-based quality models designed specifically for video are however rare, with Video-BLIINDS being one example. The Video Quality Experts Group has a dedicated working group on developing no-reference metrics (called NORM). The most traditional ways of evaluating quality of digital video processing system (e.g. a video codec) are FR-based. Among the oldest FR metrics are signal-to-noise ratio (SNR) and peak signal-to-noise ratio (PSNR), which are calculated between every frame of the original and the degraded video signal. PSNR is the most widely used objective image quality metric, and the average PSNR over all frames can be considered a video quality metric. PSNR is also used often during video codec development in order to optimize encoders. However, PSNR values do not correlate well with perceived picture quality due to the complex, highly non-linear behavior of the human visual system. With the success of digital video, a large number of more precise FR metrics have been developed.