QVRF: A Quantization-Error-Aware Variable Rate Framework for Learned Image Compression
Kedeng Tong, Yaojun Wu, Yue Li, Kai Zhang, Li Zhang, Xin Jin
Abstract
Learned image compression has exhibited promising compression performance, but variable bitrates over a wide range remain a challenge. State-of-the-art variable rate methods compromise the loss of model performance and require numerous additional parameters. In this paper, we present a Quantization-error-aware Variable Rate Framework (QVRF) that utilizes a univariate quantization regulator a to achieve wide-range variable rates within a single model. Specifically, QVRF defines a quantization regulator vector coupled with predefined Lagrange multipliers to control quantization error of all latent representation for discrete variable rates. Additionally, a reparameterization method makes QVRF compatible with round quantizer and integer entropy coding. Exhaustive experiments demonstrate that existing fixed-rate VAE-based methods equipped with QVRF can achieve wide-range continuous variable rates within a single model without significant performance degradation. Furthermore, QVRF outperforms contemporary variable-rate methods in rate-distortion performance with minimal additional parameters. The code is available at https://github.com/bytedance/QRAF.