Strategies for enhancing deep video encoding efficiency using the Convolutional Neural Network in a hyperautomation mechanism
Abstract 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 st...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-025-85602-1 |
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| author | Xiaolan Wang |
| author_facet | Xiaolan Wang |
| author_sort | Xiaolan Wang |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-6680f2f4ecff4da494737b3f884f3b8b |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-6680f2f4ecff4da494737b3f884f3b8b2025-01-12T12:17:01ZengNature PortfolioScientific Reports2045-23222025-01-0115112010.1038/s41598-025-85602-1Strategies for enhancing deep video encoding efficiency using the Convolutional Neural Network in a hyperautomation mechanismXiaolan Wang0The Higher Educational Key Laboratory for Flexible Manufacturing Equipment Integration of Fujian Province (Xiamen Institute of Technology)Abstract 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.https://doi.org/10.1038/s41598-025-85602-1Variable resolutionConvolutional Neural NetworkDepth videoVideo coding efficiencyHyperautomation mechanism |
| spellingShingle | Xiaolan Wang Strategies for enhancing deep video encoding efficiency using the Convolutional Neural Network in a hyperautomation mechanism Scientific Reports Variable resolution Convolutional Neural Network Depth video Video coding efficiency Hyperautomation mechanism |
| title | Strategies for enhancing deep video encoding efficiency using the Convolutional Neural Network in a hyperautomation mechanism |
| title_full | Strategies for enhancing deep video encoding efficiency using the Convolutional Neural Network in a hyperautomation mechanism |
| title_fullStr | Strategies for enhancing deep video encoding efficiency using the Convolutional Neural Network in a hyperautomation mechanism |
| title_full_unstemmed | Strategies for enhancing deep video encoding efficiency using the Convolutional Neural Network in a hyperautomation mechanism |
| title_short | Strategies for enhancing deep video encoding efficiency using the Convolutional Neural Network in a hyperautomation mechanism |
| title_sort | strategies for enhancing deep video encoding efficiency using the convolutional neural network in a hyperautomation mechanism |
| topic | Variable resolution Convolutional Neural Network Depth video Video coding efficiency Hyperautomation mechanism |
| url | https://doi.org/10.1038/s41598-025-85602-1 |
| work_keys_str_mv | AT xiaolanwang strategiesforenhancingdeepvideoencodingefficiencyusingtheconvolutionalneuralnetworkinahyperautomationmechanism |