ADAPTIVE MULTIMEDIA OPTIMIZATION USING GENERATIVE ADVERSARIAL NETWORKS AND ATTENTION-BASED DEEP FEATURE LEARNING FRAMEWORK

Multimedia systems have faced persistent challenges in maintaining perceptual quality under dynamic network and computational constraints. Traditional optimization techniques have struggled to preserve visual fidelity while adapting to heterogeneous content distributions. A hybrid deep learning framework was proposed to address these limitations by combining Generative Adversarial Networks (GANs) with attention-based feature learning. The proposed method, named Attention-Guided Generative Adversarial Multimedia Optimization Network (AG-GAMON), was designed to enhance spatial-temporal feature representation and improve adaptive quality control. The GAN component has been utilized to generate high-fidelity reconstructed frames, while the attention mechanism has been employed to selectively focus on semantically important regions. The discriminator has been trained to distinguish between reconstructed and original multimedia samples, ensuring improved perceptual consistency. The framework has integrated a reinforcement-based adaptive weighting strategy that has dynamically adjusted loss contributions across content types. Multimedia systems have faced persistent challenges in maintaining perceptual quality under dynamic network and computational constraints. A hybrid deep learning framework has been proposed to address these limitations by combining Generative Adversarial Networks (GANs) with attention-based feature learning. The proposed method, Attention-Guided Generative Adversarial Multimedia Optimization Network (AG-GAMON), has enhanced spatial-temporal feature representation and adaptive quality control. Experimental evaluation has demonstrated improved performance with 35.2 dB PSNR, 0.94 SSIM, 0.013 MSE, and 94 VMAF compared to CNN, LSTM, and GAN baselines.