Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The measurement of video quality for lossy and low-bitrate network transmissions is a challenging topic. Especially, the temporal artifacts which are introduced by video transmission systems and their effects on the vier's satisfaction have to be addressed.  focuses on a framework that adds a temporal distortion awareness to typical video quality measurement algorithms. A motion estimation is used to track image areas over time. Based on the motion vectors and the motion prediction error, the appearance of new image areas and the display time of objects is evaluated. Additionally, degradations which stick to moving objects can be judged more exactly. An implementation of this framework for multimedia sequences, e.g., QCIF, CIF, or VGA resolution, is presented in detail. It shows that the processing steps and the signal representations that are generated by the algorithm follow the reasoning of a human observer in a subjective experiment. The improvements that can be achieved with the newly proposed algorithm are demonstrated using the results of the Multimedia Phase I database of the Video Quality Experts Group.
['Marcus Barkowsky', 'Jens Bialkowski', 'Björn M. Eskofier', 'Roland Bitto', 'Andre Kaup']
I am writing a research paper on a idea Temporal Trajectory Aware Video Quality Measure
[{"from": "human", "value": "I am writing a research paper on a idea Temporal Trajectory Aware Video Quality Measure"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The measurement of video quality for lossy and low-bitrate network transmissions is a challenging topic. Especially, the temporal artifacts which are introduced by video transmission systems and their effects on the vier's satisfaction have to be addressed.  focuses on a framework that adds a temporal distortion awareness to typical video quality measurement algorithms. A motion estimation is used to track image areas over time. Based on the motion vectors and the motion prediction error, the appearance of new image areas and the display time of objects is evaluated. Additionally, degradations which stick to moving objects can be judged more exactly. An implementation of this framework for multimedia sequences, e.g., QCIF, CIF, or VGA resolution, is presented in detail. It shows that the processing steps and the signal representations that are generated by the algorithm follow the reasoning of a human observer in a subjective experiment. The improvements that can be achieved with the newly proposed algorithm are demonstrated using the results of the Multimedia Phase I database of the Video Quality Experts Group."}]