Unveiling the art of video enhancement: a comprehensive examination of content selection and sequencing for optimal quality in conventional and AR/VR environments

• Autorzy: Mehrunnisa Mehrunnisa , Leszczuk Mikolaj , Juszka Dawid

Identyfikatory

DOI

10.24425/ijet.2025.155450

• Języki publikacji: EN

Abstrakty

EN

The demand for high-quality video content has grown along with the rise of new technologies. The quality of visual content directly impacts user engagement and satisfaction, highlighting a clear correlation between user expectations and content delivery. Recent studies stress how important it is to pick the right content, especially in fields such as signal processing and multimedia communication. But there are challenges, such as inconsistent content selection, a lack of standards, and not enough data. Using generative AI and machine learning can help address these issues. By embracing technology-driven, inclusive and teamwork-based methods, this review paper reviews better content and sequence choices in both traditional and AR/VR setups for video enhancement. The need for high-quality content has increased with time due to the emergence of new technologies. User engagement and satisfaction are directly proportional to the quality of visual, revealing a direct proportionality between user expectations and content delivery. Recent research in digital media has emphasized the importance of selecting a particular type of content, leading to an optimized user experience. Signal processing, multimedia communication, and image processing have been significant areas of interest for researchers in which content selection is of great importance. Factors such as motion characteristics and visual complexity must be considered for precise results. The main consequence emphasizes the focus on dynamic content, diversity, and UGC as a significant area of interest. Compared to the current literature, challenges such as content selection variability, no standardized criteria, and limited data sets that serve as benchmarks must be considered. Integrating machine learning algorithms into data sets alongside scenario-based criteria can be an essential solution to such problems. Adherence to technology-driven, inclusive, and collaborative approaches leads to a better outcome that ensures productivity.

Słowa kluczowe

EN

Quality of experience; video quality evaluation; video content selection; traditional media; AR/VR media

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2025
• Tom: Vol. 71, No. 3
• Strony: 32
• Opis fizyczny: Biliogr. 50 poz., tab., rys.

Twórcy

autor

Mehrunnisa Mehrunnisa

• AGH University of Krakow, Poland

autor

Leszczuk Mikolaj

• AGH University of Krakow, Poland

autor

Juszka Dawid

• AGH University of Krakow, Poland

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).