Learned Progressive Image Compression With Spatial Autoregression

Wenxin Tian, Shaohui Li, Wenrui Dai, Cewu Lu, Weisheng Hu, Lin Zhang, Junfeng Du, Hongkai Xiong

Abstract: Entropy modeling plays an important role in estimating the rates of latent representations and optimizing the rate-distortion performance for learned image compression. Autoregression modules are demonstrated to eliminate spatial/channel-wise redundancy of latent representations in fixed-rate learned image compression. However, it cannot be efficiently achieved in progressive coding due to the high computational complexity raised by element-wise probability prediction. In this paper, we propose a learned progressive image compression method that enables spatial autoregression for entropy modeling. Specifically, we develop a novel codeword alignment scheme to prevent coding redundancy and achieve efficient autoregression of latent representations in different quality layers. Consequently, conditional probability estimation for the latent prediction can be achieved based on spatial autoregression in a layer-wise manner. We further extend the proposed method by dead-zone quantizers to obtain promoted rate-distortion performance. The proposed method is a successful attempt to enable spatial autoregression in learned progressive coding and further bridge the performance gap with fixed-rate models. Experimental results show that it outperforms traditional methods such as JPEG and BPG, as well as recent fine-grained learned progressive coding models DPICT and PLONQ in terms of rate-distortion performance.