Table 7: Papers with machine learning methods for assessing QoE.

Paper# video sequencesResolutionCode cSSIMVQMPSRNMSEBRFRPLR value (decodificables frames rate)QP Playout interruptionsDelayJitterBWMLBSGOP Machine learning Assess MOS/DMOS?Correlation performance
PCCSROCCRMSEOR

He et al. [17], 20121QCIFMPEG4XXXXXProbabilistic Neural Network (PNN)Yes0.9286NoNoNo
Kipli et al. [18], 2012N/DNo dataNo dataXXXBPNNYes0.891NoNoNo
Singh et al. [19], 2012 4HD-720pH.264XXXXRNNYesNoNo0.37No
Lia et al. [20], 20114No dataNo dataBPNN NoNoNoNoNo
Khan at al. [21], 20106QCIFH.264XXXANFISYes 0.8717 No0.2812No
Du et al. [22], 20091SDNo dataXXXXXXBPNNYesNoNoNoNo
Piamrat et al. [23], 20091No dataH.264XXXPSQA with RNNYesNoNoNoNo
Khan et al. [24], 20083CIFMPEG4XXXXXANFISYesR = 0.8056 for MOS predicted versus MOS Measure and R = 0.9229 for predicted versus measure NoRMSE = 0.1846 for MOS predicted versus MOS measure and RMSE = 0.06234 for predicted versus measure No
Rubino et al. [25], 2004Only VoIPNo dataNo dataXXRNNNoNoNoNoNo

BR: bitrate, FR: frame rate, PLR: Packet Loss Rate, QP: Quantization Parameter, BW: bandwidth, MLBS: Mean Loss Burst Size, GOP: Group of Pictures, PCC: Pearson correlation coefficient, SROCC: Spearman rank order correlation coefficient, RMSE: Root Mean Square Error, and OR: Outlier Ratio.