Low-rate Image Compression with Super-resolution Learning

Wei Gao, Lvfang Tao, Linjie Zhou, Dinghao Yang, Xiaoyu Zhang, Zixuan Guo

Abstract: In this paper, we propose an end-to-end learned image compression framework for low-rate scenarios. Based on variational autoencoder, our method features a pair of compact-resolution and super-resolution networks, a set of hyper and main codec networks, and a conditional context model. The learning process of this framework is facilitated with integrated non-local attention modules and phase congruency priors. Multiple models are obtained from training with different hyper-parameters, and are jointly used in the image-level model selection process for rate control, which ensures that the bit-rate constraint of the CLIC challenge is satisfied. Experimental results demonstrate that the proposed method can achieve an averaged multi-scale structural similarity (MS-SSIM) score of 0.9648 with bit-rate consumption of 0.1499 bits per pixel, which outperforms the BPG image coding method significantly.