Efficient Pruning Method for Learned Lossy Image Compression Models Based on Side Information

In recent years, deep learning-based lossy image compression have achieved great success. However, the problem of their huge overhead in terms of computational and parametric costs has still not been adequately addressed. Inspired by the classical image compression methods, deep learning based models are usually combined with an entropy model to maintain the compression performance. Existing methods also introduce side information to serve as a prior on the parameters of the entropy model, which have achieved better rate-distortion performance. Based on the role of side information in learned image compression models, we propose an efficient pruning method for such models. In particular, the proposed pruning approach automatically searches for the optimal decoder architecture based on the extent to which each hidden layer in the decoder utilizes side information. The experiment results demonstrate the effectiveness of the proposed method and show that it outperforms all existing related studies in terms of compression performance.