End-to-End Quality Controllable Image Compression

Abstract

Quality control is an important topic in many application scenarios such as medical image compression. To achieve quality control in image compression network, in this paper, we propose a quality controllable image compression network, Quality Controllable Variational Autoencoder (QCVAE). QC-VAE consists of the Quality-Feature-Level (QFL) model we proposed and the Hyperprior Continuously Variable Rate (HCVR) image compression network which can adapt to multiple target qualities with only one single model. Note that even if the target quality is the same, different images should be quantized with different levels. With the help of the QFL model, we can obtain the estimated corresponding quantization level of the input image under the target quality, the selected level will be used to control the quantization loss. By this means, the QC-VAE can adapt to the target quality with high accuracy. Experimental results have shown that compared with the HCVR model, the proposed QC-VAE achieves accurate quality control without rate-distortion (RD) performance loss, indicating its superiority.