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, the reparameterization method makes QVRF compatible with a round quantizer. 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.

翻译：学习型图像压缩已展现出有前景的压缩性能，但宽范围的可变码率仍是一个挑战。现有先进的可变码率方法会损害模型性能，且需要大量额外参数。本文提出一种基于量化误差感知的可变码率框架（QVRF），该框架利用单变量量化调节器a在单一模型中实现宽范围的可变码率。具体而言，QVRF定义了一个与预定义拉格朗日乘子耦合的量化调节器向量，以控制所有潜在表示的量化误差，从而实现离散可变码率。此外，重参数化方法使QVRF能够兼容取整量化器。大量实验表明，现有基于固定码率VAE的方法在嵌入QVRF后，可在单一模型中实现宽范围的连续可变码率，且无明显性能退化。此外，QVRF在极少量额外参数下，其率失真性能优于当代可变码率方法。