AVT: Au-Assisted Visual Transformer For Facial Expression Recognition

End-to-end compression methods designed for the texture image have achieved excellent coding performances. Due to the characteristic differences between the depth map and the texture image, the texture-oriented methods have limitations in depth map compression. To address this problem, this paper proposes a texture-guided end-to-end depth map compression network (TDMC-Net). Specifically, the proposed TDMC-Net is mainly composed of the texture-guided transform module (TTM) which performs the nonlinear transform with providing the textual context to reduce the redundancy in depth feature, and a texture-guided conditional entropy model (TCEM) which is designed to improve the entropy model by introducing the texture conditional prior. Experimental results show that the proposed TDMC-Net boosts the depth coding efficiency by utilizing the texture information and achieves superior performance.