The rise in the consumption of multimedia content has resulted in the demand to provide an exceptional user experience. However, modelling user-perceived Quality of Experience (QoE) presents a complex challenge. This fuels ongoing efforts to understand and measure QoE. To assess it, researchers rely on both subjective quality assessment (where users view and rate videos) and objective quality assessment (where quality metrics are designed to calculate perceived video quality). Although subjective evaluation is essential for mapping user experience to objective metrics, many studies omit the analysis of the impact of content preferences on user experience. This oversight limits our understanding of how video content influences QoE perception.To address this limitation, we created a multi-content video dataset with impairments based on realistic network conditions. Furthermore, we conducted a subjective study in a controlled environment evaluating the impact of user content category preferences and user video preference on QoE. One of our initial findings is that the actual video denoted as uninteresting by users had the most negative impact on the overall user QoE, but had no impact on user perception of other video degradations.

The impressive results achieved by language recognition using a generative pre-trained transformer have led to divided opinions on whether or not the Turing test has finally been passed. After understanding the working principles of the GPT programs, it was remarked that the tokenization concept, used by GPT, resulted in the loss of the word-to-letter relationship. Through about 36 specially prepared anagrams with a description of a term in a verse in the languages of the South Slavs, it was shown that ChatGPT and similar programs are far more capable of understanding the semantic connection between words and allusions than in performing the relatively simple task of searching for an adequate word from the offered letters.

The continuous rise of multimedia entertainment has led to an increased demand for delivering outstanding user experience of multimedia content. However, modeling user-perceived Quality of Experience (QoE) is a challenging task, resulting in efforts for better understanding and measurement of user-perceived QoE. To evaluate user QoE, subjective quality assessment, where people watch and grade videos, and objective quality assessment in which videos are graded using one or many objective metrics are conducted. While there is a plethora of video databases available for subjective and objective video quality assessment, these videos are artificially infused with various temporal and spatial impairments. Videos being assessed are artificially distorted with startup delay, bitrate changes, and stalls due to rebuffering events. To conduct a more credible quality assessment, a reproduction of original user experiences while watching different types of streams on different types and quality of networks is needed. To aid current efforts in bridging the gap between the mapping of objective video QoE metrics to user experience, we developed DashReStreamer, an open source framework for re-creating adaptively streamed video in real networks. The framework takes inputs in the form of video logs captured by the client in a non-regulated setting, along with an .mpd file or a YouTube URL. The ultimate result is a video sequence that encompasses all the data extracted from the video log. DashReStreamer also calculates popular video quality metrics like PSNR, SSIM, MS-SSIM, and VMAF. Finally, DashReStreamer allows creating impaired video sequences from the popular streaming platform YouTube. As a demonstration of framework usage, we created a database of 332 realistic video clips, based on video logs collected from real mobile and wireless networks. Every video clip is supplemented with bandwidth trace and video logs used in its creation and also with objective metrics calculation reports. In addition to dataset, we performed subjective evaluation of video content, assessing its effect on overall user QoE. We believe that this dataset and framework will allow the research community to better understand the impacts of video QoE dynamics.