Rate Distortion Performance

Rate-distortion performance in image and video compression focuses on optimizing the trade-off between data size (rate) and reconstruction quality (distortion). Current research emphasizes improving this trade-off through deep learning, particularly using variational autoencoders (VAEs), transformers, and convolutional neural networks (CNNs), often in hybrid architectures. These advancements aim to surpass traditional codecs in efficiency, leading to smaller file sizes for comparable image or video quality, with applications ranging from improved data storage to efficient media transmission. A key challenge remains balancing improved rate-distortion performance with computational efficiency for practical deployment on various hardware platforms.