Ai-guided vectorization for efficient storage and semantic retrieval of visual data
Bibliographic record
Abstract
The rapid growth of multimedia content has increased the demand for effective methods to reduce storage requirements while maintaining quality and enabling fast data transmission. Existing standards and generative model approaches often involve high computational cost, require extensive parameter tuning, and produce inconsistent results, particularly in environments with limited processing resources. This paper presents a convolutional autoencoder framework for reducing the storage footprint of image and video data. The proposed method is designed for efficient integration with existing storage and retrieval systems. Several autoencoder architectures are developed and evaluated on diverse datasets including CelebA, IMDb Faces, Oxford Flowers 102, MNIST, and UCF101. The results show 56.6% to 70.8% for image data volume with minimal degradation in perceptual quality. The system incorporates a latent representation module that supports compact storage, efficient indexing, and accurate reconstruction. These capabilities are essential for practical deployment in multimedia platforms. Experimental evaluation demonstrates that the proposed approach performs competitively with recent techniques while providing greater consistency and reduced computational overhead. In comparison to generative models, the method achieves a higher peak signal to noise ratio and improved structural fidelity. This study offers a practical and reproducible solution for storage reduction, well suited for large scale image and video archiving and retrieval under constrained or high-throughput conditions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".