Strategies for enhancing deep video encoding efficiency using the Convolutional Neural Network in a hyperautomation mechanism.

With ongoing social progress, three-dimensional (3D) video is becoming increasingly prevalent in everyday life. As a key component of 3D video technology, depth video plays a crucial role by providing information about the distance and spatial distribution of objects within a scene. This study focuses on deep video encoding and proposes an efficient encoding method that integrates the Convolutional Neural Network (CNN) with a hyperautomation mechanism. First, an overview of the principles underlying CNNs and the concept of hyperautomation is presented, and the application of CNNs in the intra-frame prediction module of video encoding is explored. By incorporating the hyperautomation mechanism, this study emphasizes the potential of Artificial Intelligence to enhance encoding efficiency. Next, a CNN-based method for variable-resolution intra-frame prediction of depth video is proposed. This method utilizes a multi-level feature fusion network to reconstruct coding units. The effectiveness of the proposed variable-resolution coding technique is then evaluated by comparing its performance against the original method on the high-efficiency video coding (HEVC) test platform. The results demonstrate that, compared to the original test platform method (HTM-16.2), the proposed method achieves an average Bjøntegaard delta bit rate (BDBR) savings of 8.12% across all tested video sequences. This indicates a significant improvement in coding efficiency. Furthermore, the viewpoint BDBR loss of the variable-resolution coding method is only 0.15%, which falls within an acceptable margin of error. This suggests that the method is both stable and reliable in viewpoint coding, and it performs well across a broad range of quantization parameter settings. Additionally, compared to other encoding methods, the proposed approach exhibits superior peak signal-to-noise ratio, structural similarity index, and perceptual quality metrics. This study introduces a novel and efficient approach to 3D video compression, and the integration of CNNs with hyperautomation provides valuable insights for future innovations in video encoding.