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

Area of Science:

• Computer Vision
• Machine Learning
• Data Compression

Background:

• Rapid growth in multimedia content necessitates efficient storage and transmission methods.
• Existing compression techniques and generative models often suffer from high computational costs and inconsistent results, especially in resource-constrained environments.

Purpose of the Study:

• To present a convolutional autoencoder framework for reducing the storage footprint of image and video data.
• To develop a method for efficient integration with existing storage and retrieval systems.
• To offer a practical and reproducible solution for large-scale multimedia archiving under constrained or high-throughput conditions.

Main Methods:

• Development and evaluation of several convolutional autoencoder architectures.
• Testing on diverse datasets including CelebA, IMDb Faces, Oxford Flowers 102, MNIST, and UCF101.
• Incorporation of a latent representation module for compact storage, efficient indexing, and accurate reconstruction.

Main Results:

• Achieved 56.6% to 70.8% reduction in image data volume with minimal perceptual quality degradation.
• Demonstrated competitive performance against recent techniques with greater consistency and reduced computational overhead.
• Outperformed generative models in peak signal-to-noise ratio and structural fidelity.

Conclusions:

• The proposed convolutional autoencoder framework provides an effective and efficient solution for multimedia data storage reduction.
• The latent representation module enhances practical deployment capabilities for multimedia platforms.
• The method is well-suited for large-scale image and video archiving and retrieval, especially under demanding conditions.