Traditional Codecs

Traditional codecs, aiming to efficiently compress and decompress data like audio, video, and images, are undergoing significant refinement driven by the integration of neural networks. Current research focuses on hybrid approaches combining model-based codecs (e.g., MPEG standards like H.266/VVC) with neural networks (e.g., autoencoders, GANs, and transformer-based architectures) to improve rate-distortion performance and address limitations like cross-platform compatibility and low-delay requirements. This work is crucial for optimizing data transmission in resource-constrained environments and improving the efficiency of machine vision tasks that rely on compressed data.