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In an earlier study, we gathered perceptual evaluations of the audio, video,and audiovisual quality for 360 audiovisual content. This paper investigatesperceived audiovisual quality prediction based on objective quality metrics andsubjective scores of 360 video and spatial audio content. Thirteen objectivevideo quality metrics and three objective audio quality metrics were evaluatedfor five stimuli for each coding parameter. Four regression-based machinelearning models were trained and tested here, i.e., multiple linear regression,decision tree, random forest, and support vector machine. Each model wasconstructed using a combination of audio and video quality metrics and twocross-validation methods (k-Fold and Leave-One-Out) were investigated andproduced 312 predictive models. The results indicate that the model based onthe evaluation of VMAF and AMBIQUAL is better than other combinations ofaudio-video quality metric. In this study, support vector machine provideshigher performance using k-Fold (PCC = 0.909, SROCC = 0.914, and RMSE = 0.416).These results can provide insights for the design of multimedia quality metricsand the development of predictive models for audiovisual omnidirectional media.

Perceptual Evaluation of 360 Audiovisual Quality and Machine Learning Predictions