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In this paper, we explore the impact of frame rate and quantization on perceptual quality of a video. We propose to use the product of a spatial quality factor that assesses the quality of decoded frames without considering the frame rate effect and a temporal correction factor, which reduces the quality assigned by the first factor according to the actual frame rate. We find that the temporal correction factor follows closely an inverted falling exponential function, whereas the quantization effect on the coded frames can be captured accurately by a sigmoid function of the peak signal-to-noise ratio. The proposed model is analytically simple, with each function requiring only a single content-dependent parameter. The proposed overall metric has been validated using both our subjective test scores as well as those reported by others. For all seven data sets examined, our model yields high Pearson correlation (higher than 0.9) with measured mean opinion score (MOS). We further investigate how to predict parameters of our proposed model using content features derived from the original videos. Using predicted parameters from content features, our model still fits with measured MOS with high correlation.