Content Adaptive Checkerboard Context Model for Learned Image Compression

Learned image compression methods are becoming popular and have achieved excellent performance, of which joint context and hyperprior architectures are the mainstream. In order to avoid the time-consuming serial decoding pipeline introduced by the autoregressive context model, the checkerboard context model (CCM) is proposed to implement fast two-pass coding. However, CCM sets half of the latents as anchors to extract spatial context for the other non-anchors, which is rough and redundant. We propose a more precise and flexible content adaptive checkerboard context model to decrease the numbers and bit consumption of anchors. By introducing pseudo-anchors for simple regions in latents, our method can preserve the capability of fast two-pass coding and outperform CCM in Rate-Distortion performance on several baseline models with negligible computational overhead.