implicit representation taking pixel coordinates as input and use a simple MLP to output pixel RGB value, implicit representation taking frame index as input and use a MLP. In Table4.5, we apply common normalization layers in NeRV block. We speedup NeRV by running it in half precision (FP16). traditional frame-based video compression approaches (H.264, HEVC ). Besides compression, we demonstrate the generalization of NeRV for video denoising. As an image-wise implicit representation, NeRV shares lots of similarities with pixel-wise implicit visual representations[44, 48] which takes spatial-temporal coordinates as inputs. Given a frame index, NeRV outputs the corresponding RGB image. 1. In contrast, our NeRV representation, trains a purposefully designed neural network composed of MLPs and convolution layers, and takes the frame index as input and directly outputs all the RGB values of that frame. As the most popular media format nowadays, videos are generally viewed as frames of sequences. More recently, deep learning-based visual compression approaches have been gaining popularity. The zoomed areas show that our model produces fewer artifacts and the output is smoother. Finally, we use entropy encoding to further compress the model size. Unlike conventional representations that treat videos as. In this work, we present a novel neural representation for videos, NeRV, which encodes videos into neural networks. As the first image-wise neural representation, NeRV generally achieves comparable performance with traditional video compression techniques and other learning-based video compression approaches. We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. Given a video with size of THW, pixel-wise representations need to sample the video THW times while NeRV only need to sample T times. It is worth noting that when BPP is small, NeRV can match the performance of the state-of-the-art method, showing its great potential in high-rate video compression. On the contrary, our NeRV can output frames at any random time index independently, thus making parallel decoding much simpler. 21 May 2021, 20:48 (modified: 22 Jan 2022, 15:59), neural representation, implicit representation, video compression, video denoising. where q is the q percentile value for all parameters in . Video encoding in NeRV is simply fitting a neural network to video frames and decoding process is a simple feedforward operation. 2 Spatial representations are organized along the long axis of the hippocampus. Use the "Report an Issue" link to request a name change. to produce the evaluation metrics for H.264 and HEVC. PSNR and MS-SSIM are adopted for evaluation of the reconstructed videos. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. As shown in Table3, the decoding video quality keeps increasing when the training epochs are longer. When BPP becomes large, the performance gap is mostly because of the lack of full training due to GPU resources limitations. Such a long pipeline makes the decoding process very complex as well. [better source needed] Finally, we provide ablation studies on the UVG dataset. NeRV takes the time embedding as input and outputs the corresponding RGB Frame. However, the redundant parameters within the network structure can cause. Please note that we only explore these three common compression techniques here, and we believe that other well-established and cutting edge model compression algorithm can be applied to further improve the final performances of video compression task, which is left for future research. NeRV allows us to convert the video compression problem to a model compression problem, allowing us to leverage standard model compression tools and reach comparable performance with conventional video compression methods, e.g., H.264[58], and HEVC[47]. representation, NeRV output the whole image and shows great efficiency compared Specifically, we train our model with a subset of frames sampled from one video, and then use the trained model to infer/predict unseen frames given an unseen interpolated frame index. Our proposed NeRV enables us to reformulate the video compression problem into model compression, and utilize standard model compression techniques. We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. We test a smaller model on Bosphorus video, and it also has a better performance compared to H.265 codec with similar BPP. We hypothesize that the normalization layer reduces the over-fitting capability of the neural network, which is contradictory to our training objective. Similarly, we can interpret a video as a recording of the visual world, where we can find a corresponding RGB state for every single timestamp. 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. We present a method that takes as input a set of images of a scene illuminated by unconstrained known lighting, and produces as output a 3D representation that can be rendered from novel viewpoints under arbitrary lighting conditions. Abstract We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. 2022 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. Recently, the image-wise implicit neural representation of videos, NeRV, has gained popularity for its promising results and swift speed compared to regular pixel-wise implicit. For example, conventional video compression methods are restricted by a long and complex pipeline, specifically designed for the task. First, we concatenate 7 videos into one single video along the time dimension and train NeRV on all the frames from different videos, which we found to be more beneficial than training a single model for each video. We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. Specifically, we explore a three-step model compression pipeline: model pruning, model quantization, and weight encoding, and show the contributions of each step for the compression task. Deep neural networks have achieved remarkable success for video-based ac Succinct representation of complex signals using coordinate-based neural , which consists of multiple convolutional layers, taking the normalized frame index as the input and output the corresponding RGB frame. Advances in Neural Information Processing Systems 34 (NeurIPS 2021), Hao Chen, Bo He, Hanyu Wang, Yixuan Ren, Ser Nam Lim, Abhinav Shrivastava. We provide the experiment results for video compression task on MCL-JCL[54]dataset in Figure11 and Figure11. In NeRV, each video V={vt}Tt=1RTHW3 is represented by a function f:RRHW3, where the input is a frame index t and the output is the corresponding RGB image vtRHW3. Given a frame index, NeRV outputs the corresponding RGB image. As a simple and efficient video representation, HNeRV also shows decoding advantages for speed, flexibility, and deployment, compared to traditional codecs (H.264, H.265) and learning-based compression methods. Compare with state-of-the-arts methods. Recently, the image-wise implicit neural representation of videos, NeRV, has gained popularity for its promising results and swift speed compared to regular pixel-wise implicit representations. Based on the magnitude of weight values, we set weights below a threshold as zero. H.264 and HEVC are performed with medium preset mode. We also explore NeRV for video temporal interpolation task. task. However, the redundant parameters within the network structure can cause a large model size when scaling up for desirable performance. Input embedding. When compare with state-of-the-arts, we run the model for 1500 epochs, with batchsize of 6. 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). Although deep neural networks can be used as universal function approximators[21], directly training the network f with input timestamp t results in poor results, which is also observed by[39, 33]. Therefore, unlike traditional video representations which treat videos as sequences of frames, shown in Figure 1 (a), our proposed NeRV considers a video as a unified neural network with all information embedded within its architecture and parameters, shown in Figure1 (b). We also show that NeRV can outperform standard denoising methods. Key frame can be reconstructed by its encoded feature only while the interval frame reconstruction is also based on the reconstructed key frames. 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. For example, conventional video compression Unfortunately, like many advances in deep learning for videos, this approach can be utilized for a variety of purposes beyond our control. 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. Therefore, we stack multiple NeRV blocks following the MLP layers so that pixels at different locations can share convolutional kernels, leading to an efficient and effective network. Abstract and Figures 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. We apply several common noise patterns on the original video and train the model on the perturbed ones. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. Activation layer. In Table. Inspired by the super-resolution networks, we design the NeRV block, illustrated in Figure, For NeRV, we adopt combination of L1 and SSIM loss as our loss function for network optimization, which calculates the loss over all pixel locations of the predicted image and the ground-truth image as following. What is a video? These can be viewed as denoising upper bound for any additional compression process. Given a frame index, NeRV outputs the corresponding RGB image. We convert video compression problem to model compression (model pruning, model quantiazation, and weight encoding etc. After model pruning, we apply model quantization to all network parameters. Once the video is encoded into a neural network, this network can be used as a proxy for video, where we can directly extract all video information from the representation. This work proposes a patch-wise solution to represent a video with implicit neural representations, PS-NeRV, which represents videos as a function of patches and the corresponding patch coordinate, and achieves excellent reconstruction performance with fast decoding speed. We study how to represent a video with implicit neural representations (INRs). With such a representation, we can treat videos as neural networks, simplifying several video-related tasks. Network Architecture. Open Access. For experiments on Big Buck Bunny, we train NeRV for 1200 epochs unless otherwise denoted. Acknowledge We hope that this paper can inspire further research works to design novel class of methods for video representations. (c) and (e) are denoising output for DIP, Input embedding ablation. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Pixel-wise representations output the RGB value for each pixel, while NeRV outputs a whole image, demonstrated in Figure2. Training speed means time/epoch, while encoding time is the total training time. Given an input timestamp t, normalized between (0,1], the output of embedding function () is then fed to the following neural network. PE means positional encoding, E. Agustsson, D. Minnen, N. Johnston, J. Balle, S. J. Hwang, and G. Toderici, Scale-space flow for end-to-end optimized video compression, M. Antonini, M. Barlaud, P. Mathieu, and I. Daubechies, R. Banner, I. Hubara, E. Hoffer, and D. Soudry, Scalable methods for 8-bit training of neural networks, R. Chabra, J. E. Lenssen, E. Ilg, T. Schmidt, J. Straub, S. Lovegrove, and R. Newcombe, Deep local shapes: learning local sdf priors for detailed 3d reconstruction, G. Chen, W. Choi, X. Yu, T. Han, and M. Chandraker, Learning efficient object detection models with knowledge distillation, Proceedings of the 31st International Conference on Neural Information Processing Systems, Z. Cheng, H. Sun, M. Takeuchi, and J. Katto, Learning image and video compression through spatial-temporal energy compaction, E. Denton, W. Zaremba, J. Bruna, Y. LeCun, and R. Fergus, Exploiting linear structure within convolutional networks for efficient evaluation, S. Dieleman, J.