Corner-to-Center Long-range Context Model for Efficient Learned Image
  Compression
Yang Sui Ding Ding Xiang Pan Xiaozhong Xu Shan Liu Bo Yuan Zhenzhong Chen
In the framework of learned image compression, the context model plays a
pivotal role in capturing the dependencies among latent representations. To
reduce the decoding time resulting from the serial autoregressive context
model, the parallel context model has been proposed as an alternative that
necessitates only two passes during the decoding phase, thus facilitating
efficient image compression in real-world scenarios. However, performance
degradation occurs due to its incomplete casual context. To tackle this issue,
we conduct an in-depth analysis of the performance degradation observed in
existing parallel context models, focusing on two aspects: the Quantity and
Quality of information utilized for context prediction and decoding. Based on
such analysis, we propose the \textbf{Corner-to-Center transformer-based
Context Model (C$^3$M)} designed to enhance context and latent predictions and
improve rate-distortion performance. Specifically, we leverage the
logarithmic-based prediction order to predict more context features from corner
to center progressively. In addition, to enlarge the receptive field in the
analysis and synthesis transformation, we use the Long-range Crossing Attention
Module (LCAM) in the encoder/decoder to capture the long-range semantic
information by assigning the different window shapes in different channels.
Extensive experimental evaluations show that the proposed method is effective
and outperforms the state-of-the-art parallel methods. Finally, according to
the subjective analysis, we suggest that improving the detailed representation
in transformer-based image compression is a promising direction to be explored.