toplogo
Sign In

Low-Latency Neural Stereo Streaming for Efficient Video Compression


Core Concepts
Efficient low-latency stereo video compression using neural networks.
Abstract
The rise of new video modalities like VR and AVs increases demand for efficient multi-view video compression. Existing stereo video compression methods have limitations in parallelization and runtime performance. Low-Latency neural codec for Stereo video Streaming (LLSS) introduces a bidirectional feature shifting module for efficient encoding. LLSS processes left and right views in parallel, reducing latency and improving R-D performance. LLSS outperforms existing neural and conventional codecs on common stereo video benchmarks. Contributions include a novel codec architecture, bidirectional shift module, and thorough experiments showcasing efficiency.
Stats
"LLSS processes left and right views in parallel, minimizing latency." "LLSS substantially improves R-D performance compared to existing codecs."
Quotes
"LLSS processes left and right views in parallel, minimizing latency." "LLSS substantially improves R-D performance compared to existing codecs."

Key Insights Distilled From

by Qiqi Hou,Far... at arxiv.org 03-27-2024

https://arxiv.org/pdf/2403.17879.pdf
Low-Latency Neural Stereo Streaming

Deeper Inquiries

How can the efficiency of LLSS impact the adoption of VR and AV technologies?

The efficiency of Low-Latency Neural Stereo Streaming (LLSS) can have a significant impact on the adoption of Virtual Reality (VR) and Autonomous Vehicle (AV) technologies. By providing fast and efficient low-latency stereo video streaming, LLSS addresses the crucial need for high-quality video compression with minimal delay. In VR applications, where immersive user experiences are paramount, LLSS can ensure that high-resolution stereo videos are transmitted with minimal latency, enhancing the overall VR experience. For AV technologies, such as autonomous vehicles equipped with stereo cameras, LLSS can provide cost-effective and time-sensitive safety analyses during vehicle operation. The efficient compression and low latency of LLSS can enable real-time processing of stereo video data, contributing to safer and more reliable autonomous driving systems.

What are the potential drawbacks or limitations of parallel processing in stereo video compression?

While parallel processing in stereo video compression offers several advantages, such as reduced latency and improved efficiency, there are also potential drawbacks and limitations to consider: Complexity: Implementing parallel processing in stereo video compression can increase the complexity of the system, requiring careful synchronization and coordination between parallel processes. Resource Intensive: Parallel processing may require additional computational resources, such as multiple processors or GPUs, which can increase the cost of implementation. Data Dependency: Parallel processing relies on dividing tasks into smaller sub-tasks that can be processed simultaneously. However, this approach may introduce data dependencies and synchronization issues that can impact overall performance. Scalability: Scaling parallel processing to handle larger volumes of data or more complex algorithms may pose challenges in terms of resource allocation and system optimization. Programming Complexity: Developing and optimizing parallel algorithms for stereo video compression can be more complex than sequential processing, requiring specialized knowledge and expertise.

How can the bidirectional feature shifting module in LLSS be applied to other video compression techniques?

The bidirectional feature shifting module in LLSS offers a novel approach to capturing and transferring mutual information between views in stereo video compression. This module can be applied to other video compression techniques to enhance their performance and efficiency. Here are some ways the bidirectional feature shifting module can be utilized in other video compression techniques: Improved Redundancy Reduction: The bidirectional feature shifting module can help reduce redundancy between frames in video compression, leading to more efficient encoding and decoding processes. Enhanced Information Exchange: By facilitating the exchange of information between different parts of the video frames, the bidirectional feature shifting module can improve the quality of reconstructed frames. Parallel Processing Optimization: Integrating the bidirectional feature shifting module in parallel processing architectures can enhance the efficiency of processing multiple frames simultaneously. Adaptive Compression: The bidirectional feature shifting module can adaptively adjust the compression process based on the mutual information between frames, leading to optimized compression rates and quality. Cross-View Analysis: By analyzing the correlation between features from different views, the bidirectional feature shifting module can enable more accurate disparity estimation and motion compensation in video compression techniques.
0