Unveiling the art of video enhancement: a comprehensive examination of content selection and sequencing for optimal quality in conventional and AR/VR environments
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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Polish Academy of Sciences
2025-07-01
|
| Series: | International Journal of Electronics and Telecommunications |
| Subjects: | |
| Online Access: | https://journals.pan.pl/Content/135764/32_5188_Mehrunnisa_L_sk.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849470198252306432 |
|---|---|
| author | Mehrunnisa Mikolaj Leszczuk Dawid Juszka |
| author_facet | Mehrunnisa Mikolaj Leszczuk Dawid Juszka |
| author_sort | Mehrunnisa |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ec5b6b9f5aed4bc5bdc179168ef8c9cc |
| institution | Kabale University |
| issn | 2081-8491 2300-1933 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | International Journal of Electronics and Telecommunications |
| spelling | doaj-art-ec5b6b9f5aed4bc5bdc179168ef8c9cc2025-08-20T03:25:12ZengPolish Academy of SciencesInternational Journal of Electronics and Telecommunications2081-84912300-19332025-07-01vol. 71No 3https://doi.org/10.24425/ijet.2025.155450Unveiling the art of video enhancement: a comprehensive examination of content selection and sequencing for optimal quality in conventional and AR/VR environmentsMehrunnisaMikolaj Leszczuk0Dawid Juszka1AGH University of Krakow, PolandAGH University of Krakow, PolandThe 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.https://journals.pan.pl/Content/135764/32_5188_Mehrunnisa_L_sk.pdfquality of experiencevideo quality evaluationvideo content selectiontraditional vs ar/vr media |
| spellingShingle | Mehrunnisa Mikolaj Leszczuk Dawid Juszka Unveiling the art of video enhancement: a comprehensive examination of content selection and sequencing for optimal quality in conventional and AR/VR environments International Journal of Electronics and Telecommunications quality of experience video quality evaluation video content selection traditional vs ar/vr media |
| title | Unveiling the art of video enhancement: a comprehensive examination of content selection and sequencing for optimal quality in conventional and AR/VR environments |
| title_full | Unveiling the art of video enhancement: a comprehensive examination of content selection and sequencing for optimal quality in conventional and AR/VR environments |
| title_fullStr | Unveiling the art of video enhancement: a comprehensive examination of content selection and sequencing for optimal quality in conventional and AR/VR environments |
| title_full_unstemmed | Unveiling the art of video enhancement: a comprehensive examination of content selection and sequencing for optimal quality in conventional and AR/VR environments |
| title_short | Unveiling the art of video enhancement: a comprehensive examination of content selection and sequencing for optimal quality in conventional and AR/VR environments |
| title_sort | unveiling the art of video enhancement a comprehensive examination of content selection and sequencing for optimal quality in conventional and ar vr environments |
| topic | quality of experience video quality evaluation video content selection traditional vs ar/vr media |
| url | https://journals.pan.pl/Content/135764/32_5188_Mehrunnisa_L_sk.pdf |
| work_keys_str_mv | AT mehrunnisa unveilingtheartofvideoenhancementacomprehensiveexaminationofcontentselectionandsequencingforoptimalqualityinconventionalandarvrenvironments AT mikolajleszczuk unveilingtheartofvideoenhancementacomprehensiveexaminationofcontentselectionandsequencingforoptimalqualityinconventionalandarvrenvironments AT dawidjuszka unveilingtheartofvideoenhancementacomprehensiveexaminationofcontentselectionandsequencingforoptimalqualityinconventionalandarvrenvironments |