Single model learned image compression utilizing multiple scaling factors

Abstract

Image compression is a critical task in multimedia. However, all learned-based single rate compression methods face challenges, such as prolonged training time due to the need for a dedicated model per bitrate and increased memory usage. Some variable rate methods require extra input, conditional networks, or still involve training multiple models. In this paper, we propose a unified approach using scaling factors to enable variable rate compression within a single model. The scaling factors consist of multi-gain units and quantization step size. The multi-gain units reduce redundancy in encoder and decoder representations, while the quantization step size controls quantization error. We also observe unevenness among slices in the Channel-Wise entropy model, and propose channel-wise quantization compensation by assigning specific step sizes to each slice. Our method supports continuous rate adaptation without retraining. Extensive experiments on CNN-based, Transformer-based, and CNN-Transformer mixed models demonstrate superior performance across a wide range of bitrates.