Quality of Experience of VoIP Service: A Survey of ... - IEEE Xplore

496 IEEE COMMUNICATIONS SURVEYS & TUTORIALS, VOL. 14, NO. 2, SECOND QUARTER 2012Fig. 3.Taxonomy of existing SQA methodologies.1) Signal-layer black-box approaches: The signal-layerblack box category of SQA algorithms estimates the perceivedquality by processing speech waves without specificknowledge of the underlying transport network and terminalsfeatures. They can be classified as full-reference (also termedcomparison-based, double-ended, intrusive) SQA algorithmsbecause they need both the reference and degraded speechsequences, and no-reference (also termed single-ended, nonintrusive)SQA algorithms if they only process the degradedsequences to estimate the perceptual quality.Basically, signal-layer comparison-based SQA algorithmsdivide original speech sequences into 50% overlapping framesof duration 20 ms. The reference and corresponding degradedframes are transformed into a psychoacoustic domain thatconsiders the features of the human auditory system. Next,the audible distortion between reference and degraded speechframes is calculated and recorded. The gathered distortionvalues are combined to deduce the final score. The ITU-Tdefined two SQA algorithms following such a strategy inITU-T Rec. P.861 and P.862 denoted respectively as MNBand PESQ [16], [26]. These algorithms are characterized bytheir good accuracy to estimate speech quality. However, theirhigh time and space complexities are pertinent drawbacks.To decrease computing-complexity, there is a possibility formanipulating elementary objectives voice quality metrics thatare able to capture distinct types of distortions [27]. S. Reinet al. review existing elementary objectives metrics for thesake of evaluation of voice quality over wireless data networksunder the ROHC (Robust Header Compression) strategy [28].They indicate that the parametric cepstral distance, whichis the difference in shape of original and distorted spectra,correlate with objective measures. As such, they proposea 2-order polynomial function that maps measured cepstraldistance to MOS domain [28]. Note here that it is expectedthat elementary objective metric methodologies result in alower accuracy than psychoacoustic objective metrics.Notice that comparison-based SQA algorithms synchronizereference and degraded speech sequences before processingsince the comparison should be made between the originaland the corresponding degraded speech frame. The synchronisationshould be made on a frame-by-frame basis usingtreated speech signals themselves, i.e. without further controlinformation about the temporal structure of processed speechsequences. Notice that such an alignment removes any temporaldistortion introduced by adaptive jitter buffer policies. Thisconstitutes a pertinent deficiency of prevailing comparisonbasedSQA algorithms that will be described in detail inSection 6.No-reference signal-layer SQA algorithms extract directlydistortion measures from degraded speech signals, e.g., additionalnoises, interruption, level saturation, pauses, and naturalnessof speech sound that are adequately mixed to derivethe final score [29]. Needless to say, the combination ruleand the weighting factor of each metric are obtained througha training process applied on subjective MOS scores.2) Parametric-model glass-box approaches: These quantifythe QoE of voice calls through the full characterisation ofthe underlying transport network and edge-devices using a setof dedicated parameters, such as room, circuit and quantizationnoises, packet loss, coding scheme, delay jitter, one-way delay,jitter, and talker and listener echoes. Figure 4 gives an exampleof parameters that are required for the full characterizationof an advanced Mobile and IP transport system. Suitableparameters, models, and combination rules should be selectedand specified through extensive subjective testing. Note thatparametric-model SQA algorithms are able to estimate users’satisfaction either on-line or off-line (, i.e., without consideringlive measures). Emerging on-line parametric-model SQAalgorithms will be addressed and explained later in Section 4.