Overfitting the Data: Compact Neural Video Delivery via Content-aware Feature Modulation (2108.08202v2)

Abstract: Internet video delivery has undergone a tremendous explosion of growth over the past few years. However, the quality of video delivery system greatly depends on the Internet bandwidth. Deep Neural Networks (DNNs) are utilized to improve the quality of video delivery recently. These methods divide a video into chunks, and stream LR video chunks and corresponding content-aware models to the client. The client runs the inference of models to super-resolve the LR chunks. Consequently, a large number of models are streamed in order to deliver a video. In this paper, we first carefully study the relation between models of different chunks, then we tactfully design a joint training framework along with the Content-aware Feature Modulation (CaFM) layer to compress these models for neural video delivery. {\bf With our method, each video chunk only requires less than $1\% $ of original parameters to be streamed, achieving even better SR performance.} We conduct extensive experiments across various SR backbones, video time length, and scaling factors to demonstrate the advantages of our method. Besides, our method can be also viewed as a new approach of video coding. Our primary experiments achieve better video quality compared with the commercial H.264 and H.265 standard under the same storage cost, showing the great potential of the proposed method. Code is available at:\url{https://github.com/Neural-video-delivery/CaFM-Pytorch-ICCV2021}

• The paper introduces a joint training framework with a CaFM layer that leverages DNN overfitting to compress video delivery models.
• It streams less than 1% of the original parameters while enhancing super-resolution performance compared to conventional codecs.
• The method enables efficient video delivery on resource-constrained devices and opens avenues for further optimization with meta-learning.

Overview of "Overfitting the Data: Compact Neural Video Delivery via Content-aware Feature Modulation"

The paper "Overfitting the Data: Compact Neural Video Delivery via Content-aware Feature Modulation" addresses the burgeoning demand for efficient video delivery systems leveraging the vast potential of Deep Neural Networks (DNNs). The authors propose a method to compress models used in neural video delivery by introducing a content-aware feature modulation (CaFM) layer, which is an innovative approach that potentially reduces the bandwidth and storage constraints currently experienced in delivering high-resolution video over the internet.

Core Contributions

The paper's primary contribution lies in the introduction of a joint training framework accompanied by the CaFM layer, which enables significant compression of models necessary for video streaming. This framework exploits the DNN’s overfitting capabilities to ensure high-quality video delivery by transmitting low-resolution (LR) video chunks and lightweight models, which are then used to reconstruct the original high-resolution (HR) video at the client side. The key observation is that despite being trained on different video chunks, the relation between models' feature maps remains remarkably linear and can be modeled through the CaFM layer.

Numerical Results and Claims

The authors achieve compelling numerical outcomes with their proposed method, indicating that for each video chunk, only less than 1% of the original parameters need to be streamed, all the while improving super-resolution (SR) performance. Experiments conducted across various SR backbones, video time lengths, and scaling factors substantiate these claims. The paper also compares the CaFM approach with conventional video coding standards, H.264 and H.265, demonstrating superior video quality under equivalent storage constraints. This highlights the method's potential not only in reducing the resource intensity of video streaming but also in setting new benchmarks in video coding efficiency.

Implications and Future Directions

The implications of this research extend beyond practical applications to fundamental advances in DNN-based video delivery systems. By significantly reducing the required bandwidth and computational overhead, this method opens up new possibilities for deploying SR technologies on resource-constrained devices such as mobile phones. Furthermore, the research addresses a critical gap between the promise of AI-enhanced video delivery and its real-world deployment challenges, bridging the gap with substantial quantitative backing.

The paper indicates promising avenues for future research, particularly in optimizing the neural network training process to further minimize training time and computational demands, as the current method involves considerable training efforts. The exploration of meta-learning techniques such as Model-Agnostic Meta-Learning (MAML) could further enhance the practical applicability of DNN-based SR methods in dynamic environments.

Conclusion

In summary, this paper offers a robust and innovative solution for addressing the constraints of current video delivery systems. Its methodological advancements in model compression through content-aware modulation pave the way for more efficient and scalable video delivery technologies. While the proposed framework shows significant promise, its adaptability to different neural architectures and coding standards suggests broad applicability and the potential for substantial impact in both academic and industry spheres.