Comparative Analysis of Data Compression Techniques Using Generative AI Models

JaymalaChavan

Generative AI, Data Compression, Compression Ratio, Reconstruction Quality, Computational Efficiency

The process of compression becomes vital to maximize data storage and transmission optimization and especially applies to big data environments. Digital data continues to expand rapidly thus demanding more effective compression methods to become essential. Many types of complex datasets containing high numbers of dimensions prove incompatible with standard compression methods like Huffman coding and Run-Length Encoding. Generative AI models represent new methods for compression, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Vector Quantized Auto encoders (VQ-VAEs), which are emerged as promising alternatives. This study analyzes how generative AI systems develop image and video compression process output and checks their output quality along with resource usage metrics. The main function of compression techniques is to decrease redundant data without compromising crucial information that needs for efficient storage and transmission. The advancement of generative models now enables them to acquire complicated data distributions along with compressed representations which maintain essential aspects from original data. The core purpose of this investigation is to analyze different generative AI models for data compression based on their effectiveness and evaluate their strengths and weaknesses when processing images and video content. In addition the study will identify optimal models for particular applications through performance-based assessments that measure compression effectiveness and reconstructive quality and computational efficiency. Some generative models demonstrate their competence by reaching high compression ratios alongside preserving quality standards in lossy compression operations. These models either present realistic visual reconstructions although they demand increased computational resources. This research into generative model comparison with traditional methods reveals significant findings suitable for various application-related decisions making

Volume 14 Issue 2, February-2026

Pages : 46-53