NeRV: Neural Representations for Videos (2110.13903v1)

Abstract: We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. Given a frame index, NeRV outputs the corresponding RGB image. Video encoding in NeRV is simply fitting a neural network to video frames and decoding process is a simple feedforward operation. As an image-wise implicit representation, NeRV output the whole image and shows great efficiency compared to pixel-wise implicit representation, improving the encoding speed by 25x to 70x, the decoding speed by 38x to 132x, while achieving better video quality. With such a representation, we can treat videos as neural networks, simplifying several video-related tasks. For example, conventional video compression methods are restricted by a long and complex pipeline, specifically designed for the task. In contrast, with NeRV, we can use any neural network compression method as a proxy for video compression, and achieve comparable performance to traditional frame-based video compression approaches (H.264, HEVC \etc). Besides compression, we demonstrate the generalization of NeRV for video denoising. The source code and pre-trained model can be found at https://github.com/haochen-rye/NeRV.git.

Insightful Overview of NeRV: Neural Representations for Videos

The paper "NeRV: Neural Representations for Videos" presents a novel approach to video representation by leveraging neural networks. This research challenges the conventional methodology of treating videos as sequences of frames, instead proposing an implicit neural representation model where videos are encapsulated as functions of time, parametrized through simple neural constructions.

Core Concept and Methodology

NeRV (Neural Representations for Videos) is the central innovation of this paper. Traditional video frameworks rely on explicit, frame-wise representations, which often lead to rigid pipelines, especially in complex operations such as video compression. NeRV decouples these dependencies by modeling a video as a neural network that maps a given frame index directly to an RGB image. This approach significantly reduces the computation time of both video encoding and decoding processes while improving the efficiency of video quality reconstruction.

The architecture of NeRV employs a combination of Multi-Layer Perceptrons (MLPs) and convolutional networks. With dynamic upscaling specifically advantageous in high-resolution context -- through PixelShuffle techniques -- NeRV efficiently reconstructs frames using image-wise processing, avoiding the computationally expensive pixel-wise operations.

Results and Implications

In terms of performance, NeRV demonstrates substantial improvements in both encoding and decoding speeds compared to pixel-wise representations, outpacing them by orders of magnitude. For instance, the paper reports enhancements in encoding speed by up to 70 times and decoding speed by up to 132 times. Evidence also shows that NeRV can achieve competitive video quality metrics compared to traditional methods.

NeRV presents compelling results in video compression by reformulating it as a neural model compression problem. Utilizing model pruning, quantization, and entropy encoding, NeRV achieves performance on par with established compression methods, such as H.264 and HEVC. Beyond compression, NeRV's flexible neural function implicitly supports video denoising, surpassing conventional filters without dedicated noise removal training.

Theoretical and Practical Implications

Theoretically, NeRV positions itself as a significant paradigm shift, introducing an implicit representation that encapsulates temporal information. This methodology paves the way for future research in continuous video representations where models can potentially perform concurrent operations like interpolation and super-resolution more naturally.

Practically, the implications of using NeRV extend to more streamlined video processing pipelines, which could simplify encoding infrastructures and reduce latency, beneficial for real-time applications. Additionally, the model's ability to parallelize frame generation is a notable advantage for distributed computing applications, such as cloud-based video streaming.

Future Directions

While the NeRV framework provides a robust foundation for video representation, future explorations should target optimizing neural architecture design for even higher accuracy and efficiency. Extending the current framework to incorporate spatiotemporal cues more intricately could also elevate its application in more complex tasks like action recognition or video synthesis. Furthermore, integrating state-of-the-art compression techniques into NeRV's model compression workflow could enhance its applicability in resource-constrained environments.

In conclusion, the NeRV paradigm is a methodological leap in video representation, carrying profound implications for future AI developments in multimedia processing, compression, and beyond. This paper is a testament to the ongoing evolution of neural network capabilities in addressing traditional computational challenges in innovative ways.