arXiv:2408.04889v1 Announce Type: new
Abstract: The growing demand for high-quality point cloud transmission over wireless networks presents significant challenges, primarily due to the large data sizes and the need for efficient encoding techniques. In response to these challenges, we introduce a novel system named Deep Point Cloud Semantic Transmission (PCST), designed for end-to-end wireless point cloud transmission. Our approach employs a progressive resampling framework using sparse convolution to project point cloud data into a semantic latent space. These semantic features are subsequently encoded through a deep joint source-channel (JSCC) encoder, generating the channel-input sequence. To enhance transmission efficiency, we use an adaptive entropy-based approach to assess the importance of each semantic feature, allowing transmission lengths to vary according to their predicted entropy. PCST is robust across diverse Signal-to-Noise Ratio (SNR) levels and supports an adjustable rate-distortion (RD) trade-off, ensuring flexible and efficient transmission. Experimental results indicate that PCST significantly outperforms traditional separate source-channel coding (SSCC) schemes, delivering superior reconstruction quality while achieving over a 50% reduction in bandwidth usage.
The Significance of Deep Point Cloud Semantic Transmission (PCST) in the Field of Multimedia Information Systems
As the demand for high-quality point cloud transmission over wireless networks continues to rise, researchers face significant challenges in ensuring efficient encoding techniques to handle the large data sizes involved. In response to these challenges, a novel system named Deep Point Cloud Semantic Transmission (PCST) has been introduced. This system aims to enable end-to-end wireless point cloud transmission by employing a progressive resampling framework and a deep joint source-channel (JSCC) encoder.
One of the notable multidisciplinary aspects of this system lies in its utilization of sparse convolution to project point cloud data into a semantic latent space. By doing so, the system leverages the semantic features of the point cloud, providing a more abstract representation that is easier to encode and transmit efficiently. This integration of computer vision and signal processing techniques demonstrates the importance of integrating multiple disciplines to address challenges in multimedia information systems.
In the wider field of multimedia information systems, PCST tackles the critical issue of point cloud transmission. With the increasing adoption of point clouds in various applications such as 3D modeling, virtual reality (VR), and augmented reality (AR), the efficient transmission of point cloud data becomes paramount. PCST’s ability to project point cloud data into a semantic latent space and encode the semantic features addresses this challenge directly, providing significant advancements in point cloud transmission efficiency.
Enhancing Transmission Efficiency with Adaptive Entropy-Based Approach
A key aspect of PCST that contributes to its superior performance is the adaptive entropy-based approach used to assess the importance of each semantic feature. This approach allows for varying transmission lengths based on predicted entropy, enabling the system to allocate resources more efficiently. By dynamically adapting the transmission length based on the predicted importance of each feature, PCST achieves an optimal rate-distortion (RD) trade-off, ensuring flexible and efficient transmission.
This approach has far-reaching implications not only for PCST but also for multimedia information systems as a whole. The adaptive entropy-based approach has the potential to be applied to other areas of multimedia data transmission, such as image and video compression. By dynamically allocating resources according to the importance of different parts of the data, efficiency gains can be achieved, resulting in improved transmission quality and reduced bandwidth usage.
Outperforming Traditional Coding Schemes
Experimental results indicate that PCST significantly outperforms traditional separate source-channel coding (SSCC) schemes. It not only delivers superior reconstruction quality but also achieves over a 50% reduction in bandwidth usage. This remarkable performance improvement highlights the effectiveness of the deep joint source-channel encoder in PCST, which combines encoding and transmission into a single integrated system.
In the context of augmented reality (AR), virtual reality (VR), and artificial reality systems, PCST’s ability to optimize point cloud transmission becomes crucial. These immersive multimedia applications heavily rely on the efficient transmission of 3D data to provide users with realistic and interactive experiences. PCST’s advancements in point cloud transmission efficiency directly contribute to improving the overall performance and user experience of these systems.
Conclusion
The introduction of Deep Point Cloud Semantic Transmission (PCST) addresses the challenges faced in high-quality point cloud transmission over wireless networks. Through the application of a progressive resampling framework, sparse convolution, and a deep joint source-channel encoder, PCST provides efficient transmission of point cloud data. Its adaptive entropy-based approach further enhances transmission efficiency, optimizing the rate-distortion trade-off. Experimental results demonstrate the superiority of PCST over traditional coding schemes, making it a promising solution for multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.