Title: LVQ-VAE:End-to-end Hyperprior-based Variational Image Compression with Lattice Vector Quantization. Abstract: Image compression technology has become more important research topic. In recent years, learning-based methods have been extensively studied and variational autoencoder (VAE)-based methods using hyperprior-based context-adaptive entropy model have been reported to be comparable to the latest video coding standard H.266/VVC in terms of RD performance.
   We think there is room for improvement in quantization process of latent features by adopting vector quantization (VQ). Many VAE-based methods use scalar quantization for latent features and do not exploit correlation between the features. Although there are methods that incorporate VQ into learning-based methods, to the best our knowledge, there are no studies that utilizes the hyperprior-based VAE with VQ because incorporating VQ into a hyperprior-based VAE makes it difficult to estimate the likelihood.
   In this paper, we propose a new VAE-based image compression method using VQ based latent representation for hyperprior-based context-adaptive entropy model to improve the coding efficiency. The proposed method resolves problem faced by conventional VQ-based methods due to codebook size bloat by adopting Lattice VQ as the basis quantization method and achieves end-to-end optimization with hyperprior-based context-adaptive entropy model by approximating the likelihood calculation of latent feature vectors with high accuracy using Monte Carlo integration. Furthermore, in likelihood estimation, we model each latent feature vector with multivariate normal distribution including covariance matrix parameters, which improves the likelihood estimation accuracy and RD performance.
   Experimental results show that the proposed method achieves a state-of-the-art RD performance exceeding existing learning-based methods and the latest video coding standard H.266/VVC by 18.0%.