Existing learning-based stereo image codec adopt sophisticated transformation
with simple entropy models derived from single image codecs to encode latent
representations. However, those entropy models struggle to effectively capture
the spatial-disparity characteristics inherent in stereo images, which leads to
suboptimal rate-distortion results. In this paper, we propose a stereo image
compression framework, named CAMSIC. CAMSIC independently transforms each image
to latent representation and employs a powerful decoder-free Transformer
entropy model to capture both spatial and disparity dependencies, by
introducing a novel content-aware masked image modeling (MIM) technique. Our
content-aware MIM facilitates efficient bidirectional interaction between prior
information and estimated tokens, which naturally obviates the need for an
extra Transformer decoder. Experiments show that our stereo image codec
achieves state-of-the-art rate-distortion performance on two stereo image
datasets Cityscapes and InStereo2K with fast encoding and decoding speed.

该研究提出了一种名为 CAMSIC 的立体图像压缩框架，通过引入一种新颖的内容感知蒙版图像建模（MIM）技术，以独立地将每个图像转换为潜在表示，并采用强大的无解码器 Transformer 熵模型，来捕捉立体图像中的空间位移特征，从而实现了最新的速率失真性能。