arXiv:2503.21197v1 Announce Type: new Abstract: Existing wireless video transmission schemes directly conduct video coding in pixel level, while neglecting the inner semantics contained in videos. In this paper, we propose a wireless video semantic communication framework, abbreviated as WVSC, which integrates the idea of semantic communication into wireless video transmission scenarios. WVSC first encodes original video frames as semantic frames and then conducts video coding based on such compact representations, enabling the video coding in semantic level rather than pixel level. Moreover, to further reduce the communication overhead, a reference semantic frame is introduced to substitute motion vectors of each frame in common video coding methods. At the receiver, multi-frame compensation (MFC) is proposed to produce compensated current semantic frame with a multi-frame fusion attention module. With both the reference frame transmission and MFC, the bandwidth efficiency improves with satisfying video transmission performance. Experimental results verify the performance gain of WVSC over other DL-based methods e.g. DVSC about 1 dB and traditional schemes about 2 dB in terms of PSNR.
The article titled “Wireless Video Semantic Communication: A Framework for Efficient Video Transmission” introduces a novel approach called WVSC (Wireless Video Semantic Communication) that aims to improve video transmission efficiency by incorporating semantic communication into wireless video transmission scenarios. Unlike existing methods that focus on pixel-level video coding, WVSC encodes original video frames as semantic frames, allowing for video coding at a semantic level. Additionally, WVSC introduces a reference semantic frame to replace motion vectors in common video coding methods, thereby reducing communication overhead. At the receiver end, WVSC utilizes multi-frame compensation (MFC) with a multi-frame fusion attention module to generate compensated current semantic frames. Experimental results demonstrate that WVSC outperforms other DL-based methods and traditional schemes in terms of peak signal-to-noise ratio (PSNR), achieving a gain of approximately 1 dB compared to DVSC and 2 dB compared to traditional methods.
Exploring the Potential of Wireless Video Semantic Communication Framework
Existing wireless video transmission schemes tend to focus solely on conducting video coding at the pixel level, overlooking the rich inner semantics contained within the videos. However, a new approach called Wireless Video Semantic Communication (WVSC) could change this perspective. In this paper, we propose a framework that integrates the concept of semantic communication into wireless video transmission scenarios, opening up a world of possibilities and unlocking the potential of video coding in the semantic level rather than traditional pixel level.
WVSC takes a different approach to video coding by encoding original video frames as semantic frames before conducting video coding. This compact representation allows for a more efficient and effective video coding process, as it focuses on the underlying semantics rather than pixel-level details. By leveraging semantic information, WVSC offers a unique and innovative solution for wireless video transmission.
One of the key contributions of WVSC is the introduction of a reference semantic frame to substitute motion vectors commonly used in traditional video coding methods. By doing so, WVSC is able to significantly reduce the communication overhead, leading to improved bandwidth efficiency without compromising the overall video transmission performance.
At the receiver end, the WVSC framework incorporates a multi-frame compensation (MFC) technique to produce a compensated current semantic frame. This is achieved through the use of a multi-frame fusion attention module, which intelligently utilizes information from multiple frames to enhance the quality of the received video. The combination of the reference frame transmission and the MFC technique provides a holistic solution that not only improves bandwidth efficiency but also ensures satisfactory video transmission performance.
To validate the effectiveness and performance of WVSC, extensive experimental tests were conducted. The results speak for themselves, with WVSC outperforming other deep learning-based methods such as DVSC by approximately 1 dB and traditional schemes by about 2 dB in terms of Peak Signal-to-Noise Ratio (PSNR). These results highlight the significant gain achieved by integrating semantic communication into wireless video transmission scenarios.
The implications of WVSC are far-reaching and hold great promise for various applications. For instance, in the field of real-time video streaming, WVSC can enable high-quality video transmission over limited bandwidth networks, making it ideal for live events and remote communication. Additionally, WVSC can also revolutionize the field of video surveillance by providing more efficient and accurate transmission of surveillance footage.
“WVSC offers a unique and innovative solution for wireless video transmission by focusing on the underlying semantics rather than pixel-level details. This not only improves bandwidth efficiency but also ensures satisfactory video transmission performance.”
Overall, the Wireless Video Semantic Communication framework presents a paradigm shift in wireless video transmission, paving the way for more efficient and effective utilization of available bandwidth. By harnessing the power of semantic communication and integrating it into video coding, WVSC brings us one step closer to realizing the full potential of wireless video transmission.
The paper titled “Wireless Video Semantic Communication Framework: Integrating Semantic Communication into Wireless Video Transmission” introduces a novel approach to wireless video transmission that goes beyond traditional pixel-level encoding. The authors propose a framework called WVSC, which focuses on encoding videos at the semantic level, capturing the inner semantics contained in videos.
The WVSC framework first encodes the original video frames as semantic frames, which are more compact representations of the video content. This allows for video coding to be performed at the semantic level, rather than the pixel level. By considering the semantic information, the authors aim to improve the video transmission performance and bandwidth efficiency.
One interesting aspect of the WVSC framework is the introduction of a reference semantic frame to substitute motion vectors in common video coding methods. By using a reference frame, the communication overhead is reduced, leading to improved bandwidth efficiency. This approach leverages the semantic information of the reference frame to compensate for the motion in subsequent frames.
To further enhance the performance of WVSC, the authors propose a multi-frame compensation (MFC) technique. This technique utilizes a multi-frame fusion attention module to produce a compensated current semantic frame at the receiver. By considering multiple frames, the MFC technique aims to improve the quality of the received video frames.
The experimental results presented in the paper demonstrate the effectiveness of the WVSC framework. Compared to other deep learning-based methods like DVSC, WVSC achieves a performance gain of approximately 1 dB in terms of peak signal-to-noise ratio (PSNR). Moreover, when compared to traditional video coding schemes, WVSC outperforms them by approximately 2 dB in terms of PSNR.
Overall, the WVSC framework presents a promising approach to wireless video transmission by incorporating semantic communication. By encoding videos at the semantic level and leveraging a reference frame and multi-frame compensation, WVSC achieves improved video transmission performance and bandwidth efficiency. Future research in this area could explore the application of WVSC in various wireless communication scenarios and investigate the impact of different semantic encoding techniques on video quality and bandwidth utilization.
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