NUCQ: NON-UNIFORM CONDITIONAL QUANTIZATION FOR LEARNED IMAGE COMPRESSION

Learned Image Compression (LIC) has surpassed traditional image compression in terms of Rate-Distortion (R-D) performance with strong and powerful non-linear transformations. However, most existing LIC approaches discretize the latent representation into integers. Such method remains the statistical redundancy and conditional information of image content, which could be further leveraged to systematically reduce bit rate in the lossy compression regime. In this work, we formulate the problem of Non-Uniform Conditional Quantization (NUCQ) in LIC and accordingly propose to learn a content-adaptive NUCQ scheme conditioned on image textural prior information. In our method, a parameterized quantile model is learned from the hyperprior information, thus more content adaptive quantization is realized in the latent domain. Experiments demonstrate that our proposed NUCQ improves the R-D performance of LIC by 4.83\% and 6.03\% on the Bjøntegaard-delta bit rate (BD-rate) metric concerning the MSE and MS-SSIM metric, respectively.