Variable-Rate Neural Image Compression with Joint Content-Channel Features and Accurate R-λ Model

Recently, neural image compression has received increasing attention as it has achieved superior rate-distortion performance than conventional image compression methods. However, most neural image compression models are fixed rate models, they need to train multiple models for different bit rates, which greatly increases the burden of computation and storage. While some variable-rate models scale the latent features only in the channels, which do not consider the differences in the content of each image. To address these issues, firstly we design a Content Gain Attention Module (CGAM) to learn the relationship between the content of each image and the bit rates. Then we propose a variable-rate neural image compression framework, which utilizes CGAM modules to achieve variable rate in one single model. The framework takes a source image and a Lagrange multiplier λ as inputs and outputs a compressed image with variable rates. Moreover, to achieve more precise rate control, an accurate m, R-λ model is further proposed. Besides, the Channel Gain Unit is utilized to assist variable rate, which scales latent features in a channel-wise manner. Extensive experimental results show that our method is effective and outperforms existing models.