Predicting multimedia session mos

Abstract

It is provided a method, performed by a MOS, Mean Opinion Score, estimator, for predicting a multimedia session MOS. The multimedia comprises a video and an audio, wherein video quality is represented by a list of per time unit scores of a video quality, an initial buffering event and rebuffering events in the video, and wherein audio quality is represented by a list of per time unit scores of audio quality. The method comprises: generating video features from the list of per time unit scores of the video quality; generating audio features from the list of per time unit scores of the audio quality; generating buffering features from the initial buffering event and rebuffering events in the video; and estimating a multimedia session MOS from the generated video features, generated audio features and generated buffering features by using machine learning technique.

1 . A method, performed by a MOS, Mean Opinion Score, estimator, for predicting a multimedia session MOS, wherein the multimedia comprises a video and an audio, wherein video quality is represented by a list of per time unit scores of the video quality, an initial buffering event and rebuffering events in the video, and wherein audio quality is represented by a list of per time unit scores of the audio quality, the method comprising:
 generating one or more of the group consisting of:
 video features from the list of per time unit scores of the video quality; 
 audio features from the list of per time unit scores of the audio quality; 
 buffering features from the initial buffering event and the rebuffering events in the video; and 
   estimating the multimedia session MOS from the one or more of the generated video features, generated audio features, and generated buffering features by using machine learning technique.   
     
     
         2 . The method according to  claim 1 , wherein the video features comprise a feature being a first percentile of the per unit time scores of the video quality. 
     
     
         3 . The method according to  claim 1 , wherein the video features comprise a feature being a fifth percentile of the per unit time scores of the video quality. 
     
     
         4 . The method according to  claim 1 , wherein the video features comprise a feature being a fifteenth percentile of the per unit time scores of the video quality. 
     
     
         5 . The method according to  claim 1 , wherein the step of estimating is based on a random forest based model. 
     
     
         6 . The method according to  claim 1 , wherein the buffering features comprise a feature being total buffering time. 
     
     
         7 . The method according to  claim 1 , wherein the buffering features comprise a feature being number of the rebuffering events. 
     
     
         8 . The method according to  claim 1 , wherein the buffering features comprise a feature being percentage of buffering time divided by video time. 
     
     
         9 . The method according to  claim 1 , wherein the buffering features comprise a feature being number of the rebuffering events per video length. 
     
     
         10 . The method according to  claim 1 , wherein the buffering features comprise a feature being last seen rebuffering from the end of the video. 
     
     
         11 . A MOS, Mean Opinion Score, estimator for predicting a multimedia session MOS, wherein the multimedia comprises a video and an audio, wherein video quality is represented by a list of per time unit scores of the video quality and an initial buffering event and rebuffering events in the video and wherein audio quality is represented by a list of per time unit scores of the audio quality, the MOS estimator comprising processing means and a memory comprising instructions which, when executed by the processing means, causes the MOS estimator to:
 generate one or more of the group consisting of:
 video features from the input list of per time unit scores of the video quality; 
 audio features from the input list of per time unit scores of the audio quality; 
 buffering features from the initial buffering event and the rebuffering events in the video; and 
   estimate the multimedia session MOS from the one or more of the generated video features, generated audio features, and generated buffering features by using machine learning technique.   
     
     
         12 . The MOS estimator according to  claim 11 , wherein the video features comprise a feature being a first percentile of the per unit time scores of the video quality. 
     
     
         13 . The MOS estimator according to  claim 11 , wherein the video features comprise a feature being a fifth percentile of the per unit time scores of the video quality. 
     
     
         14 . The MOS estimator according to  claim 11 , wherein the video features comprise a feature being a fifteenth percentile of the per unit time scores of the video quality. 
     
     
         15 . The MOS estimator according to  claim 11 , wherein the instructions to estimate comprise instructions which, when executed by the processing means, causes the MOS estimator to estimate using a random forest based model. 
     
     
         16 . The MOS estimator according to  claim 11 , wherein the buffering features comprise a feature being total buffering time. 
     
     
         17 . The MOS estimator according to  claim 11 , wherein the buffering features comprise a feature being number of rebuffering events. 
     
     
         18 . The MOS estimator according to  claim 11 , wherein the buffering features comprise a feature being percentage of buffering time divided by video time. 
     
     
         19 . The MOS estimator according to  claim 11 , wherein the buffering features comprise a feature being number of rebuffering events per video length. 
     
     
         20 . The MOS estimator according to  claim 11 , wherein the buffering features comprise a feature being last seen rebuffering from the end of the video. 
     
     
         21 . A MOS, Mean Opinion Score, estimator comprising:
 a generating module, configured to generate video features from an input list of per time unit scores of video quality, generate audio features from the input list of per time unit scores of audio quality and generate buffering features from an initial buffering event and rebuffering events in a video; and   a predicting module, configured to predict a multimedia session MOS from the generated video features, generated audio features and generated buffering features by using machine learning technique.   
     
     
         22 . A non-transitory computer-readable storage medium comprising a computer program product including instructions to cause at least one processor to:
 generate video features from an input list of per time unit scores of video quality;   generate audio features from the input list of per time unit scores of audio quality;   generate buffering features from an initial buffering event and rebuffering events in the video; and   estimate a multimedia session MOS, Mean Opinion Score, from the generated video features, generated audio features and generated buffering features by using machine learning technique.