# Assessment of the Quality of Video Sequences Performed by Viewers at Home and in the Laboratory
## 1 Introduction
Finally, there are papers that give a comprehensive view of the different factors that influence the degradation of the video content delivered to the user, present a broad review of objective video quality assessment methods, their classification, and performance comparison [9], and give a survey of the evolution of these methods, analyzing their characteristics, advantages, and drawbacks [10; 11]. Most of them are good enough for comparison and benchmarking purposes [12; 13; 14; 15], but some give results that present stronger correlations with quality of experience (QoE) scores, given by users during subjective quality assessment, than others. Mapping the quality of service (QoS) onto QoE allows us to build proper QoE models. However, finding general relationships between QoS and QoE is not an easy task. Sometimes, the content of the video may influence the perceptual-based quality assessment in specific circumstances [16; 17; 18; 19]. This is why big content providers and streaming platforms, which use Dynamic Adaptive Streaming over HTTP (DASH) mechanisms to provide their content via the Internet, use different coding bit rate ladders according to the video content provided [20; 21]. Furthermore, the bit rate coding ladder for specific video content may depend on the video codec [22]. The authors chose three objective quality metrics, namely, the PSNR [23], SSIM [24], and the video multimethod assessment fusion (VMAF) [25], from the long list of metrics proposed in the literature. The PSNR metric is often used because it has a clear physical meaning and is simple to calculate. It presents good results when assessing the influence of some degradation factors on the quality of specific video footage, e.g., before and after compression. However, it may not always be sufficiently correlated with subjective quality assessment scores. PSNR is memoryless, which means that it is calculated pixel by pixel, independently, for each pair of corresponding frames of the two compared videos and assumes that the video quality is independent of the spatial and temporal relationships between the samples of the source footage. Reordering the pixels in the reference and examined videos, in the same way, does not change the PSNR values, although the subjective quality may change. Moreover, it can be found in the literature that video signals are highly structured and the ordering of pixels carries important perceptual structural information about the contents of the visual scene [4]. This led to also taking into account other video quality metrics, such as SSIM and VMAF, which take into account the fact that natural image signals are highly structured and may possibly better correlate with subjective quality assessment scores [11; 24; 25; 26]. When there is no possibility to access the original footage, then no-reference (NR) image or video quality assessment methods can be used to evaluate the quality of the material delivered to the end user. The original footage may be distorted at any stage of the media delivery chain, that is, during acquisition, processing, compression, transmission, decoding, or presentation at the receiver's site. Therefore, it is important to use quality assessment methods that are based on a good representation of different types of distortions and may use them for a proper evaluation. Early NR quality assessment methods usually took into account specific distortion types, such as blur [27], blocking [28], and ringing artifacts [29]. In real situations, the distortion types are usually not known in advance; thus, recently, more attention has been paid to general-purpose NR methods. These metrics attempt to learn the knowledge when evaluating the quality of images and characterize the general rules of image distortions. On the basis of this knowledge, image quality prediction models can be established and adapted to unknown distortions [30]. There are many approaches based on deep convolutional neural networks (DCNN) NR image quality assessment (IQA) [31; 32; 33].