Deviation Control for Learned Image Compression

Yuqi Li, Haotian Zhang, Xiaomin Song, Zheng Liu, Huiming Zheng, Li Li, Dong Liu

Abstract: Most approaches in learned image compression follow the transform coding scheme. The characteristics of latent variables transformed from images significantly influence the performance of codecs. In this paper, we present visual analyses on latent features of learned image compression and find that the latent variables are spread over a wide range, which may lead to complex entropy coding processes. To address this, we introduce a Deviation Control (DC) method, which applies a constraint loss on latent features and entropy parameter μ. Training with DC loss, we obtain latent features with smaller values of coding symbols and σ, effectively reducing entropy coding complexity. Our experimental results show that the plug-and-play DC loss reduces entropy coding time by 30-40% and improves compression performance.