TPARN: A Network for Enhancing Synthetic Video Quality After 3D-HEVC Encoding

3D-High Efficiency Video Coding (3D-HEVC), as an extension of HEVC in the realm of three-dimensional video, has brought significant coding performance improvements. However, traditional 3D video coding has faced many challenges such as compression distortion in texture and depth videos, as well as non-occlusion issues in Depth Image Based Rendering (DIBR) synthesis, which directly affected the visual quality of synthesized views. A Two-Stream Pyramid Attention Residual Network (TPARN) is proposed to achieve the quality enhancement of synthesized views. First of all, the Global Residual Attention (GRA) module and the Local Pyramid Attention (LPA) module are designed to extract global context information and intricate local texture details, which achieve a comprehensive scene understanding and preserve essential details across different scales. In addition, the Pyramid Attention Module (PAM) and skip connections are utilized to extract multiscale features, promoting seamless interaction among features. Experimental results demonstrate that the proposed method effectively reduces distortion caused by view synthesis, outperforming the latest methods in terms of performance.