Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: With the rapid growth of streaming media applications, there has been a strong demand of quality-of-experience (QoE) measurement and QoE-driven video delivery technologies. Most existing methods rely on bitrate and global statistics of stalling events for QoE prediction. This is problematic for two reasons. First, using the same bitrate to encode different video content results in drastically different presentation quality. Second, the interactions beten video presentation quality and playback stalling experiences are not accounted for. In this work,  first build a streaming video database and carry out a subjective user study to investigate the human responses to the combined effect of video compression, initial buffering, and stalling.  then propose a novel QoE prediction approach named Streaming QoE Index that accounts for the instantaneous quality degradation due to perceptual video presentation impairment, the playback stalling events, and the instantaneous interactions beten them. Experimental results show that the proposed model is in close agreement with subjective opinions and significantly outperforms existing QoE models. The proposed model provides a highly effective and efficient meanings for QoE prediction in video streaming services.   1
['Zhengfang Duanmu', 'Kai Zeng', 'Kede Ma', 'Abdul Rehman', 'Zhou Wang']
I am writing a research paper on a idea A Quality-of-Experience Index for Streaming Video
[{"from": "human", "value": "I am writing a research paper on a idea A Quality-of-Experience Index for Streaming Video"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: With the rapid growth of streaming media applications, there has been a strong demand of quality-of-experience (QoE) measurement and QoE-driven video delivery technologies. Most existing methods rely on bitrate and global statistics of stalling events for QoE prediction. This is problematic for two reasons. First, using the same bitrate to encode different video content results in drastically different presentation quality. Second, the interactions beten video presentation quality and playback stalling experiences are not accounted for. In this work,  first build a streaming video database and carry out a subjective user study to investigate the human responses to the combined effect of video compression, initial buffering, and stalling.  then propose a novel QoE prediction approach named Streaming QoE Index that accounts for the instantaneous quality degradation due to perceptual video presentation impairment, the playback stalling events, and the instantaneous interactions beten them. Experimental results show that the proposed model is in close agreement with subjective opinions and significantly outperforms existing QoE models. The proposed model provides a highly effective and efficient meanings for QoE prediction in video streaming services.   1"}]