# Ai-guided vectorization for efficient storage and semantic retrieval of visual data

## TL;DR

This paper introduces an efficient method using convolutional autoencoders to reduce the storage size of images and videos while maintaining quality and enabling fast retrieval.

## Contribution

A novel convolutional autoencoder framework is proposed for compact storage and semantic retrieval of visual data with reduced computational overhead.

## Key findings

- The method achieves 56.6% to 70.8% reduction in image data volume with minimal perceptual quality loss.
- The system supports compact storage, efficient indexing, and accurate reconstruction of visual data.
- The approach outperforms generative models in peak signal to noise ratio and structural fidelity.

## 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.