Deep Learning-Enabled Efficient Storage and Retrieval of Video Streams in the Cloud
Bibliographic record
Abstract
In recent years, the rapid growth of video data has presented challenges for efficient storage and retrieval in cloud-based systems. This research proposes a novel approach that utilizes machine learning techniques to address these challenges. The objective is to develop a robust framework that optimizes storage utilization, enhances retrieval efficiency, and maintains video quality with minimal latency. Machine learning algorithms are employed for tasks such as video compression, content analysis, and metadata extraction, enabling effective storage and retrieval mechanisms. The framework leverages advanced video analytics algorithms to extract relevant features, incorporating them into the storage and retrieval process for efficient indexing and organization of video content. The framework adapts to changing workloads and dynamically allocates resources for optimal performance in cloud environments. Extensive experiments and comparisons with existing methods validate the proposed framework, demonstrating significant improvements in storage utilization, retrieval performance, and scalability. Specifically, our framework achieves an average improvement of 25% in storage efficiency compared to another method. It also demonstrates faster retrieval speeds, with an improvement of approximately 10-20%, and achieves higher video quality ratings, with an improvement of approximately 10-20% compared to existing methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".