Flexible Coding Order for Learned Image Compression

Learned image compression (LIC) methods have made significant advances in recent years. In LIC, entropy model is an essential component, which utilizes conditional information to predict the probability distribution over the latent space. In the entropy models, many context models follow a spatially autoregressive paradigm, which leads to sequential coding. The autoregressive coding order, however, may be neither optimal nor efficient. We conduct an elaborate study on coding orders in LIC entropy models, shedding light on the potential for improving compression performance by adapting the coding order. We present Mask Modeling Context Model (MMCM), a transformer-based context model designed with a Patch-restricted Iterative Mask Modeling (PIMM) training strategy. Through training to predict the probability distribution of randomly masked tokens, we are able to use a patch-level arbitrary coding order to encode/decode the latent space in a few iterative steps. Additionally, we employ offline and online rate-distortion optimization to adaptively select the appropriate coding order for each image. Our extensive experimental results demonstrate that the proposed method achieves a better rate-distortion performance than the autoregressive context model while requiring less compression complexity.