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Efficient Stereo RGB-D Video Compression for Stereoscopic Teleconferencing
核心概念
A novel sandwiched video compression scheme that upgrades a 2D video codec to efficiently compress and deliver stereo RGB-D video for stereoscopic teleconferencing, achieving 29.3% bit-rate saving over existing solutions while maintaining the same level of rendering quality.
要約
The paper proposes a novel sandwiched video compression scheme for stereo RGB-D video streaming. The key idea is to wrap a standard video codec (e.g. H.264, HEVC) with a pair of neural network-based pre- and post-processors.
The preprocessor takes the stereo RGB-D input, transforms the depth maps to a canonical coordinate system, and generates neural codes that can be efficiently compressed by the video codec. The postprocessor then reconstructs the color and depth from the compressed neural codes for novel view rendering.
The authors develop several techniques to improve the compression performance:
- Joint processing of color and geometry to enable smart bit-allocation and cross-view/cross-modality redundancy reduction.
- A disparity warping-based distortion loss function to improve the depth quality in the context of rendering.
- Transforming depth maps to world-space coordinates to facilitate stereo alignment and improve the rate-distortion performance.
Experiments on both synthetic and real-captured datasets show that the proposed scheme can achieve 29.3% bit-rate saving over existing solutions (H.264 simulcast, MV-HEVC) at the same level of rendering quality. The authors also demonstrate the generalization ability of the pre- and post-processors to work with different video codecs.
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arxiv.org
One-Click Upgrade from 2D to 3D: Sandwiched RGB-D Video Compression for Stereoscopic Teleconferencing
統計
Our method reduces the bit-rate over existing solutions by 29.3% while maintaining the same level of rendering quality.
Transforming depth maps to world-space coordinates helps improve the rate-distortion performance, especially at higher bit-rates.
Jointly processing color and geometry is important for stereo RGB-D compression, as it outperforms methods that handle them separately.
引用
"Our stereo RGB-D video codec has the following advantages: 1) We lift the complex compression workload onto an optimally engineered video codec, which is usually efficiently implemented in hardware. It keeps our system efficient while offering great compression ratio. 2) The neural processor pair learns to conduct smart bit-allocation and redundancy reduction, greatly alleviating the bandwidth pressure; 3) In contrast to existing standardized multi-view and 3D video codecs [24], our method does not require further changes made to the hardware implementation."
"Experimental results show that our method reduces the bit-rate over existing solutions by 29.3%, while maintaining the same level of rendering quality."
深掘り質問
How can the proposed sandwiched compression scheme be extended to support more advanced features like region-of-interest coding or adaptive bit-allocation based on the scene content?
The proposed sandwiched compression scheme can be extended to support more advanced features like region-of-interest coding by incorporating additional neural network modules that focus on identifying and encoding specific regions of interest within the frame. These modules can be trained to prioritize certain areas of the image based on factors such as motion, complexity, or importance to the overall scene. By dynamically allocating more bits to these regions, the system can ensure that critical details are preserved while reducing the bit-rate in less important areas.
Adaptive bit-allocation based on the scene content can be achieved by integrating scene analysis algorithms into the pre-processor neural network. These algorithms can analyze the content of the scene in real-time and adjust the bit-allocation strategy accordingly. For example, in a scene with high motion, more bits can be allocated to ensure smooth motion representation, while in a static scene, fewer bits can be allocated to reduce redundancy and optimize compression efficiency.
How can the proposed sandwiched compression scheme be extended to support more advanced features like region-of-interest coding or adaptive bit-allocation based on the scene content?
When scaling to higher resolutions or more complex camera configurations, the current approach may face several challenges and limitations. One challenge is the increased computational complexity and memory requirements associated with processing larger and more detailed frames. The neural networks in the pre- and post-processors may struggle to handle the additional data efficiently, leading to longer processing times and higher resource utilization.
Another challenge is maintaining the same level of performance and quality at higher resolutions. As the resolution increases, the neural networks may need to be retrained on larger datasets to ensure optimal performance. Additionally, more complex camera configurations, such as multiple camera angles or depths, can introduce additional complexities in the encoding and decoding processes, requiring more sophisticated algorithms and architectures to handle the increased data complexity.
How can the proposed sandwiched compression scheme be extended to support more advanced features like region-of-interest coding or adaptive bit-allocation based on the scene content?
The neural pre- and post-processors can be further optimized to reduce their computational complexity and memory footprint by implementing techniques such as model pruning, quantization, and architecture simplification. Model pruning involves removing unnecessary connections or neurons from the neural networks to reduce the overall model size and computational requirements. Quantization techniques can be applied to reduce the precision of the network parameters, leading to smaller memory footprint and faster inference times.
Additionally, architecture simplification techniques, such as reducing the number of layers or parameters in the neural networks, can help streamline the processing pipeline and improve efficiency. By optimizing the neural networks for specific hardware platforms and deployment scenarios, the overall system can be made more suitable for real-time deployment on resource-constrained devices.
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