Real-time Streaming Video Denoising with Bidirectional Buffers (2207.06937v1)

Abstract: Video streams are delivered continuously to save the cost of storage and device memory. Real-time denoising algorithms are typically adopted on the user device to remove the noise involved during the shooting and transmission of video streams. However, sliding-window-based methods feed multiple input frames for a single output and lack computation efficiency. Recent multi-output inference works propagate the bidirectional temporal feature with a parallel or recurrent framework, which either suffers from performance drops on the temporal edges of clips or can not achieve online inference. In this paper, we propose a Bidirectional Streaming Video Denoising (BSVD) framework, to achieve high-fidelity real-time denoising for streaming videos with both past and future temporal receptive fields. The bidirectional temporal fusion for online inference is considered not applicable in the MoViNet. However, we introduce a novel Bidirectional Buffer Block as the core module of our BSVD, which makes it possible during our pipeline-style inference. In addition, our method is concise and flexible to be utilized in both non-blind and blind video denoising. We compare our model with various state-of-the-art video denoising models qualitatively and quantitatively on synthetic and real noise. Our method outperforms previous methods in terms of restoration fidelity and runtime. Our source code is publicly available at https://github.com/ChenyangQiQi/BSVD

• The paper presents a Bidirectional Buffer Block that fuses past and future frames to enhance video denoising in real-time.
• It introduces a pipeline-style inference scheme that processes streaming video with constant memory usage for resource-constrained applications.
• The framework outperforms state-of-the-art models in both restoration quality and speed, demonstrating robust handling of diverse noise conditions.

An Analysis of "Real-time Streaming Video Denoising with Bidirectional Buffers"

The paper "Real-time Streaming Video Denoising with Bidirectional Buffers" presents a novel framework for video denoising, focusing on the efficiency and fidelity of processing streaming video data in real-time. The core idea is the development of the Bidirectional Streaming Video Denoising (BSVD) framework, which aims to harness past and future temporal data for high-fidelity denoising without the computation inefficiencies typically associated with sliding-window methods.

Key Contributions:

1. Bidirectional Buffer Block: At the heart of the BSVD framework is the Bidirectional Buffer Block, a novel component designed to enable bidirectional temporal fusion. This is particularly noteworthy because previous systems, like MoViNet, deemed such fusion inapplicable for streaming video. The Bidirectional Buffer Block allows the BSVD framework to maintain computation efficiency while leveraging both past and future frames to improve image fidelity.
2. Pipeline-style Inference Scheme: The framework introduces a pipeline-style inference scheme that processes video streams in constant memory. Unlike MIMO approaches, which experience linear memory consumption growth with increased clip lengths, BSVD maintains constant memory footprint, making it suitable for resource-constrained real-time applications.
3. Performance and Efficiency: By training the model with Temporal Shift Modules (TSM) for distributed parallelism and replacing these with Bidirectional Buffer Blocks during inference, the authors present a method that combines MIMO's strengths with pipeline-style execution's efficiency. This allows the framework to surpass state-of-the-art models in both restoration quality and computational speed, demonstrating superior quantitative (PSNR) and qualitative results on datasets like DAVIS and Set8.
4. Versatility in Noise Handling: The authors emphasize the framework’s flexibility in dealing with non-blind and blind denoising for synthetic Gaussian and real-world noise, proving its robustness across a wide range of conditions and tasks.

Implications and Future Directions:

The introduction of efficient and effective denoising methods for streaming data has significant implications for various multimedia applications, such as live sports broadcasting and telecommunications. The BSVD framework, by reducing memory usage and processing time, opens up opportunities for deploying denoising algorithms on edge devices, where resources are limited.

There is potential for future research to refine and extend the framework's capabilities, possibly exploring more advanced temporal fusion techniques or integrating with other video processing tasks, like super-resolution or video compression, to create comprehensive multimedia processing solutions. Additionally, future versions could explore adaptive strategies for handling diverse noise characteristics in more dynamic and unpredictable environments.

Overall, the "Real-time Streaming Video Denoising with Bidirectional Buffers" paper offers a robust framework that balances efficiency with high-quality video output, laying a strong foundation for real-time multimedia applications. As we move forward, the research community could explore further enhancements and validate the applicability across more varied use cases and environments.