Streaming video incurs many distortions during processing, compression, storage, and transmission, all of which can reduce the user's perceived video quality. Developing adaptive video transmission methods that increase the efficient use of existing bandwidth and reduce storage space while preserving visual quality requires quality metrics that accurately describe how people perceive distortion. A severe problem for developing new video quality metrics is limited data on how the human visual system processes spatial and temporal information simultaneously. The problem is exacerbated by the fact that the few data recognized by the scientific community, collected in the middle of the last century, used the ideas of obsolete display technology and were subject to medical intervention during collection, which does not guarantee a proper description of the conditions under which media content is currently consumed. As a result, modern video quality metrics do not provide stable and reliable data for predicting the subjective assessment of user quality. This research aims to investigate how the metrics being developed are made more efficient for assessing video quality by including new data from the psychophysical early vision model in the metrics. The work proposed in this thesis comprises three main contributions: Firstly, the development of a novel method, software, and test equipment for research and measurement of the characteristics of the human visual system using modern display systems. Secondly, the refinement of the parameters of a multidimensional model of human contrast sensitivity appropriate to modern display technology of viewing conditions. The contrast sensitivity function works like a filter through which visual stimuli must pass to be perceived by the observer. Only video artefacts in the passband region can be humanly perceived. Thirdly, the creation of a new full-reference and the first non-reference video quality metrics which consider the psychophysical features of the user's video experience. That provides stability in predicting the user's subjective rating of a video. Among the three contributions of this thesis, first, a method for researching and measuring the characteristics of human visual systems on modern displays. In the proposed thesis, 27,840 visibility thresholds of spatio-temporal sinusoidal variations were measured by a new method using different spatial sizes and temporal modulation rates. The obtained data is 96% more than any current contrast sensitivity function dataset and best describes a human's perception of video artefacts on a modern display. A multidimensional model of human contrast sensitivity in modern conditions of video content presentation is proposed for the first time based on new large-scale data and demonstrated that the presented visibility model has a distinct advantage for further development of media content transfer technologies. Since there is a limited number of video evaluation metrics based on fundamental knowledge about the work of the human visual system, a new full-reference metric is herein proposed. This proposed video quality metric extends the peak signal-to-noise ratio metric to include human visual system features. Finally, a new non-reference video quality metric that includes the psychophysical features of the user's video experience with stability in predicting the user's subjective rating of a video is proposed. The experimental results show that the proposed video quality metric achieves 81% more consistent performance in predicting user subjective quality among commonly used non-reference video quality metrics and comparable consistent performance to full-reference metrics on three independent video datasets. The thesis also presents a large-scale database suitable for testing video streaming quality under video compression with artefacts, forming a learning base for future video quality metrics. The final dataset comprises 4.1 million video quality perceptual thresholds. The new database will contribute to the solution of a strong need for non-reference video quality metrics for user-generated video content to prevent loss of video quality caused by distortion during recording, compression and signal transmission.