Comparative Analysis of Power Consumption and Resource Utilization in Open-Source and Proprietary Media Players while using Raw Videos
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study evaluates and compares the power consumption and resource utilization of open-source and proprietary media players during the playback of a large raw video file. Using real-time monitoring tools like HWiNFO, key metrics such as GPU power consumption, CPU power consumption, memory usage, and CPU usage percentage were collected and analyzed. The experiment was conducted on a system powered by a 12th Gen Intel(R) Core(TM) i7-12700H processor, and the media players were tested with a 2-minute, 14-second raw video file in .MOV format. A statistical analysis using t-tests was performed to assess the significance of the differences between the two categories. The results indicated that open-source media players generally exhibit lower GPU and CPU power consumption, with a potential for saving energy. Long-term power consumption analysis further demonstrated that users could achieve significant energy savings by opting for open-source media players, making them more suitable for energy-conscious environments. These findings highlight the trade-offs between power efficiency and performance while playing raw videos.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it