Variable Bitrate Image Compression With Quality Scaling Factors

Recently, learned image compression has emerged with significant coding efficiency improvement, and even shown noticeable gains over the state-of-the-art traditional codecs. In the mean time, most existing methods need to train separate models for different bitrate target. In this paper, we propose to embed a set of quality scaling factors (SFs) into learned image compression network, by which we can encode images across an entire bitrate range with a single model. This solution offers the comparable performance with those default approaches requiring multiple bitrate dependent models, and reduces the complexity significantly for practical implementation. Our work also demonstrates the generalization for various compression network structures, image contents, and training loss functions.