# Survey on Quality of Experience Evaluation for Cloud-Based Interactive Applications
## 5 Strategies for QoE Measurement in CIAs
### Strategies Based on Audovisual Degradation Measures
The PSNR is more common because of its simple calculation and because it is a metric traditionally used in the video field. However, the SSIM has higher sensitivity compared to the PSNR for detecting small variations in video sources [91]. [PERSON] et al. [42] used the PSNR as a measure of QoE in remote desktop CIAs by comparing two video streams. They compared the Amazon Workspaces stream measured at the server with the received stream measured at the user's end. They then obtained the PSNR measurements to assess the impact of different network conditions. [PERSON] et al. mapped these PSNR measurements to an MOS scale value. In their study, [PERSON] et al. [39] employed the SSIM, PSNR, and FPS together to evaluate the QoE in cloud gaming services. Such proposals offer a promising way to assess QoE. However, they require development and optimisation to be able to compute the PSNR in real time. The ITU-T proposed an algorithm to evaluate the quality of audiovisual content, called the perceptual evaluation of video quality (PEVQ), which some authors used to quantify QoE in CIAs [92]. The PEVQ is an algorithm designed for the evaluation of streaming video and is a solution that is not often adopted in the field of CIA research. The PEVQ is a standard metric that estimates video quality by providing an MOS value at the output. This requires comparing the original, undegraded reference signal with the signal received by the client. Proposals using this metric require instrumentation from both video sources to calculate it. The ITU-T offers the PEVQ not only for the assessment of visual degradation but also for audio degradation. In [93], the perceptual objective listening quality analysis (POLQA) was used to measure the audio degradation in content after receipt by the user. The algorithm uses the audio source and received audio source for its calculation to evaluate the sampling frequency, compression, and synchronisation. The metric provides an output with an estimated value of the MOS experienced by the user. In [37], [PERSON] et al. employed the PSNR, FPSs, and SSIM to analyse the user experience in a remote residential desktop environment. In [46], [PERSON] et al. also employed the SSIM, PSNR, and PEVQ in their study on cloud gaming. The authors proposed a strategy to optimise game rendering by delegating part of it to the thin client. Their proposal utilised several configurable parameters. The study analysed the impact of the values of these parameters and the effectiveness of their proposal compared to the state of the art. using audiovisual degradation metrics. These metrics require the video sources of the cloud server and the client, so their implementation in real environments is difficult. However, they provide a concrete QoE value. Regarding audio, in [33], [PERSON] et al. used three different metrics to compare original and user-perceived audio: Weighted Spectral Slope (WSS) [94], Log-Likelihood Ratio (LLR) [95], and Virtual Speech Quality Objective Listener (ViSQOL) [96]. The study focused on remote desktop environments. The WSS provides a distance measurement between the original signal and the one received by the user by obtaining a weighted difference between spectral slopes in different frequency bands. The spectral slope is obtained as the difference between adjacent spectral magnitudes in decibels. The LLR also provides a measure that quantifies the spectral differences between the reference signal and the compressed signal. The ViSQOL metric models the perception of human speech quality using a spectrotemporal measure of the similarity between a reference and a test speech signal. Similar to previous proposals, these metrics require the audio sources of the cloud server and the client, which makes their implementation in real environments difficult. Therefore, there are some common drawbacks of the proposals based on audiovisual degradation measures. The main one is that the proposed measures require obtaining audio or video signals from both the client and the server to compare degradation.