A Novel Three-Tier Deep Learning Framework for Efficient Data Compression with Multi-Objective Optimization

Abstract

This paper presents Hierarchical Compress, a novel hierarchical deep autoencoder architecture leveraging multi-head attention mechanisms and residual connections for data compression optimization. Our framework implements a three-tier encoder structure with convolutional feature extraction, transformer-based context modeling, and variational bottleneck optimization, integrated with adversarial training for perceptual quality preservation. Testing on ImageNet (14M images), UCI Time Series Data (500GB), and Common Crawl archive (100TB), the system achieves a 48% improvement in compression ratios while maintaining 0.98% data integrity through a novel loss function combining reconstruction fidelity and KL-divergence. The architecture's lightweight decoder, using depth-wise separable convolutions and skip connections, achieves 35ms decompression speed and 94% resource utilization, significantly outperforming current DRL models. Comparative analysis shows substantial improvements over existing systems: 65% better efficiency than ResMan, 55% faster processing than DeepRA, and 72% higher throughput than Rule-Based systems. Our key innovation combines MSE, perceptual loss, and adversarial components in a hybrid loss function, with an adaptive attention mechanism for dynamic compression optimization, establishing new benchmarks in intelligent data compression.