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In this paper, a review on existing methods of extending image quality metric to video quality metric is given. It is found that three processing steps are usually involved which include the temporal channel decomposition, temporal masking and error pooling. They are utilized to extend our previously proposed image quality metric, which separately evaluates additive impairments and detail losses, to video quality metric. The resultant algorithm is tested on subjective video database LIVE and shows a good performance in matching subjective ratings.