Open Access. 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). However, the redundant parameters within the network structure can cause a large model size when scaling up for desirable performance. Specifically, we use model pruning and quantization to reduce the model size without significantly deteriorating the performance. videos in neural networks. In our experiments, we train the network using Adam optimizer[26] with learning rate of 5e-4. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. Network Architecture. Abstract: We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. We first conduct ablation study on video Big Buck Bunny. We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. We study how to represent a video with implicit neural representations (INRs). Given a frame index, NeRV outputs the corresponding RGB image. Besides video compression, we also explore other applications of the NeRV representation for the video denoising task. Given a noisy video as input, NeRV generates a high-quality denoised output, without any additional operation, and even outperforms conventional denoising methods. With such a representation, we can treat videos as neural networks, simplifying several video-related tasks. We hope that this paper can inspire further research works to design novel class of methods for video representations. Given a frame index, NeRV outputs the corresponding RGB image. 36 PDF Decomposing Motion and Content for Natural Video Sequence Prediction Since most video frames are interval frames, their decoding needs to be done in a sequential manner after the reconstruction of the respective key frames. where b and l are hyper-parameters of the networks. log files (tensorboard, txt, state_dict etc . With such a representation, we show that by simply applying general model compression techniques, NeRV can match the performances of traditional video compression approaches for the video compression task, without the need to design a long and complex pipeline. Given a frame index, NeRV outputs the corresponding RGB image. With such a representation, we can treat videos as neural networks, simplifying several video-related tasks. We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. First, we use the following command to extract frames from original YUV videos, as well as compressed videos to calculate metrics: Then we use the following commands to compress videos with H.264 or HEVC codec under medium settings: where FILE is the filename, CRF is the Constant Rate Factor value, and EXT is the video container format extension. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. 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. Specifically, median filtering has the best performance among the traditional denoising techniques, while NeRV outperforms it in most cases or is at least comparable without any extra denoising design in both architecture design and training strategy. Finally, we explore the effectiveness of HNeRV on downstream tasks such as video compression and video inpainting. As listed in Table5, the PSNR of NeRV output is usually much higher than the noisy frames although its trained on the noisy target in a fully supervised manner, and has reached an acceptable level for general denoising purpose. where q is the q percentile value for all parameters in . checkpoint/ directory contains some pre-trained model on big buck bunny dataset. This project was partially funded by the DARPA SAIL-ON (W911NF2020009) program, an independent grant from Facebook AI, and Amazon Research Award to AS. It has been widely applied in many 3D vision tasks, such as 3D shapes[16, 15], 3D scenes[45, 25, 37, 6], and appearance of the 3D structure[33, 34, 35]. Then, we present model compression techniques on NeRV in Section3.2 for video compression. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. Typically, a video captures a dynamic visual scene using a sequence of frames. Figure6 shows the full compression pipeline with NeRV. Video encoding in NeRV is simply fitting a neural network to video frames and decoding process is a simple feedforward operation. 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. The main differences between our work and image-wise implicit representation are the output space and architecture designs. Before the resurgence of deep networks, handcrafted image compression techniques, like JPEG. Unlike conventional representations that treat PS-NeRV: Patch-wise Stylized Neural Representations for Videos, E-NeRV: Expedite Neural Video Representation with Disentangled If we have a model for all (x,y) pairs, then, given any x, we can easily find the corresponding y state. 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. i.e., Bilinear Pooling, Transpose Convolution, and PixelShuffle[43]. 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). DIP emphasizes that its image prior is only captured by the network structure of Convolution operations because it only feeds on a single image. Requests for name changes in the electronic proceedings will be accepted with no questions asked. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. Compare with state-of-the-arts methods. 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. As the most popular media format nowadays, videos are generally viewed as frames of sequences. Specifically, in NeRV, we use Positional Encoding[33, 52, 48] as our embedding function. For fair comparison, we train SIREN and FFN for 120 epochs to make encoding time comparable. By contrast, NeRV is able to handle this naturally by keeping training because the full set of consecutive video frames provides a strong regularization on image content over noise. For example, conventional video compression methods are restricted by a long and complex pipeline, specifically designed for the task. There are some limitations with the proposed NeRV. 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. Different output space also leads to different architecture designs, NeRV utilizes a MLP + ConvNets architecture to output an image while pixel-wise representation uses a simple MLP to output the RGB value of the pixel. 70x, the decoding speed by 38x to 132x, while achieving better video quality. In contrast, given a neural network that encodes a video in NeRV, we can simply cast the video compression task as a model compression problem, and trivially leverage any well-established or cutting edge model compression algorithm to achieve good compression ratios. Figure6 shows the results of different pruning ratios, where model of 40% sparsity still reach comparable performance with the full model. Fig. More recently, deep learning-based visual compression approaches have been gaining popularity. where T is the frame number, f(t)RHW3 the NeRV prediction, vtRHW3 the frame ground truth, is hyper-parameter to balance the weight for each loss component. NeRV [5,6], RGBNeRV2 T H W T H W NeRVT NeRV NeRVMLP+ConvNetsMLPRGB NeRV Compare with pixel-wise implicit representations. We also demonstrate the flexibility of NeRV by exploring several applications it affords. Given a parameter tensor. Given a frame index, NeRV outputs the corresponding RGB image. We speedup NeRV by running it in half precision (FP16). 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]. It is designed for production environments and is optimized for speed and accuracy on a small number of training images. Abstract:We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. Given a frame index, NeRV outputs the corresponding RGB image. We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. Finally, we provide ablation studies on the UVG dataset. Compared to image-wise neural representation, NeRV imrpoves encoding speed by 25 to 70, decoding speed by 38 to 132. With such a representation, we can treat videos as neural networks, simplifying several video-related tasks. On a 19201080 video, given the timestamp index t, we first apply a 2-layer MLP on the output of positional encoding layer, then we stack 5 NeRV blocks with upscale factors 5, 3, 2, 2, 2 respectively. C. Jiang, A. Sud, A. Makadia, J. Huang, M. Niener, T. Funkhouser, Local implicit grid representations for 3d scenes, Adam: a method for stochastic optimization, Quantizing deep convolutional networks for efficient inference: a whitepaper, MPEG: a video compression standard for multimedia applications, J. Liu, S. Wang, W. Ma, M. Shah, R. Hu, P. Dhawan, and R. Urtasun, Conditional entropy coding for efficient video compression, G. Lu, W. Ouyang, D. Xu, X. Zhang, C. Cai, and Z. Gao, Dvc: an end-to-end deep video compression framework, UVG dataset: 50/120fps 4k sequences for video codec analysis and development, Proceedings of the 11th ACM Multimedia Systems Conference, B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, Nerf: representing scenes as neural radiance fields for view synthesis, M. Niemeyer, L. Mescheder, M. Oechsle, and A. Geiger, Differentiable volumetric rendering: learning implicit 3d representations without 3d supervision, M. Oechsle, L. Mescheder, M. Niemeyer, T. Strauss, and A. Geiger, Texture fields: learning texture representations in function space, A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, PyTorch: an imperative style, high-performance deep learning library, Advances in Neural Information Processing Systems 32, H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch-Buc, E. Fox, and R. Garnett (Eds. However, the redundant parameters within the network structure can cause a large model size when scaling up for desirable performance. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. Although its main target is image denoising, NeRV outperforms it in both qualitative and quantitative metrics, demonstrated in Figure10. For loss objective in Equation2, is set to 0.7. We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. Since Huffman Coding is lossless, it is guaranteed that a decent compression can be achieved without any impact on the reconstruction quality. index as input. We propose a novel image-wise neural representation (NeRV) to encodes videos in neural networks, which takes frame index as input and outputs the corresponding RGB image. Although explicit representations outperform implicit ones in encoding speed and compression ratio now, NeRV shows great advantage in decoding speed. ), and reach comparable bit-distortion performance with other methods. several video-related tasks. Classical INRs methods generally utilize MLPs to map input coordinates to output pixels. Figure9 shows visualizations for decoding frames. NeRV architecture is illustrated in Figure2, (b). Finally, more advanced and cutting the edge model compression methods can be applied to NeRV and obtain higher compression ratios. Please note that although Transpose convolution[12] reach comparable results, it greatly slowdown the training speed compared to the PixelShuffle. Without the noise prior, it has to be used with fixed iterations settings, which is not easy to generalize to any random kind of noises as mentioned above. model_nerv.py contains the dataloader and neural network architecure data/ directory video/imae dataset, we provide big buck bunny here checkpoints/ directory contains some pre-trained model on big buck bunny dataset As a fundamental task of computer vision and image processing, visual data compression has been studied for several decades. NeRV shows good advantage over coordinate-based representation in decoding speed, encoding time and quality, and perform well in video compression and denoising tasks. where round is rounding value to the closest integer, bit the bit length for quantized model, max and min the max and min value for the parameter tensor , scale the scaling factor. Implicit neural representation is a novel way to parameterize a variety of signals. Traditional video compression frameworks are quite involved, such as specifying key frames and inter frames, estimating the residual information, block-size the video frames, applying discrete cosine transform on the resulting image blocks and so on. Our model compression composes of four standard sequential steps: video overfit, model pruning, weight quantization, and weight encoding as shown in Figure3. denoising. Most recently,[13] demonstrated the feasibility of using implicit neural representation for image compression tasks. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. Conclusion. Abstract and Figures We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. We compare with H.264[58], HEVC[47], STAT-SSF-SP[61], HLVC[60], Scale-space[1], and Wu et al. Given a frame index, NeRV outputs the corresponding RGB image. The goal of model compression is to simplify an original model by reducing the number of parameters while maintaining its accuracy. We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. Unfortunately, like many advances in deep learning for videos, this approach can be utilized for a variety of purposes beyond our control. Video compression visulization. The source code and pre-trained model can be found at https://github.com/haochen-rye/NeRV.git. As the most popular media format nowadays, videos are generally viewed as frames of sequences. NeRV: Neural Representations for Videos (NeurIPS 2021) Project Page | Paper | UVG Data. In Table. 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]. We test a smaller model on Bosphorus video, and it also has a better performance compared to H.265 codec with similar BPP. Video encoding in NeRV is simply fitting a neural network to video frames and decoding process is a simple feedforward operation. Model Compression. Upscale layer. decoding process is a simple feedforward operation. For NeRV architecture, there are 5 NeRV blocks, with up-scale factor 5, 3, 2, 2, 2 respectively for 1080p videos, and 5, 2, 2, 2, 2 respectively for 720p videos. 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. For experiments on Big Buck Bunny, we train NeRV for 1200 epochs unless otherwise denoted. Acknowledgement. It naturally . Hopefully, this can potentially save bandwidth, fasten media streaming, which enrich entertainment potentials. sequences, we represent videos as neural networks taking frame index as input. Given a frame index, NeRV outputs the corresponding RGB image. With such a representation, we can treat videos as neural networks, simplifying several video-related tasks. We then compare with state-of-the-arts methods on UVG dataset. Our method represents the scene as a continuous volumetric function parameterized as MLPs whose inputs are a 3D . Given a frame index, NeRV outputs the corresponding RGB image. For video compression, the most common practice is to utilize neural networks for certain components while using the traditional video compression pipeline. https://github.com/haochen-rye/NeRV.git. We also show that NeRV can outperform standard denoising methods. 1. model_nerv.py contains the dataloader and neural network architecure. Video encoding in NeRV is simply fitting a neural network to video frames and decoding process is a simple feedforward operation. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. But the training data of NeRV contain many video frames, sharing lots of visual contents and consistences. [59]. For example, conventional video compression methods are restricted by a long and complex pipeline, specifically designed for the task. Given a neural network fit on a video, we use global unstructured pruning to reduce the model size first. Model Pruning. 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 $\textbf{25}\times$ to $\textbf{70}\times$, the decoding speed by $\textbf{38}\times$ to $\textbf{132}\times$, while achieving better video quality. NeRV achieves comparable or better denosing performance for most noises, without special denoising design, which comes totally from data regularization and architecture regularization. 2022 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. We take SIREN[44] and NeRF[33] as the baseline, where SIREN[44] takes the original pixel coordinates as input and uses sine activations, while NeRF[33], adds one positional embedding layer to encode the pixel coordinates and uses ReLU activations. 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. data/ directory video/imae dataset, we provide big buck bunny here. We compare with other methods for decoding time under a similar memory budget. Unlike conventional representations that treat videos as. 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. Key frame can be reconstructed by its encoded feature only while the interval frame reconstruction is also based on the reconstructed key frames. We study how to represent a video with implicit neural representations (INRs). Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. These can be viewed as denoising upper bound for any additional compression process. 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. 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. to produce the evaluation metrics for H.264 and HEVC. For fine-tune process after pruning, we use 50 epochs for both UVG and Big Buck Bunny. 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. For example, conventional video compression We conduct extensive experiments on popular video compression datasets, such as UVG. 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. 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 provide the architecture details in Table11. In contrast, with NeRV, we can use any neural network compression As a result, image prior is captured by both the network structure and the training data statistics for NeRV. During training, no masks or noise locations are provided to the model, i.e., the target of the model is the noisy frames while the model has no extra signal of whether the input is noisy or not. This can be viewed as a distinct advantage over other methods. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. Video encoding in NeRV is simply fitting a neural network to video frames and decoding process is a simple feedforward operation. method as a proxy for video compression, and achieve comparable performance to We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. Our key sight is that by directly training a neural network with video frame index and output corresponding RGB image, we can use the weights of the model to represent the videos, which is totally different from conventional representations that treat videos as consecutive frame sequences. Through Equation4, each parameter can be mapped to a bit length value. In this work, we present a novel neural representation for videos, NeRV, which encodes videos into neural networks. We first present the NeRV representation in Section3.1, including the input embedding, the network architecture, and the loss objective. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. We implement our model in PyTorch, We compare NeRV with pixel-wise implicit representations on Big Buck Bunny video. For input embedding in Equation1, we use b=1.25 and l=80 as our default setting. First, to achieve the comparable PSNR and MS-SSIM performances, the training time of our proposed approach is longer than the encoding time of traditional video compression methods. Papers With Code is a free resource with all data licensed under. [better source needed] As a general representation for videos, NeRV also shows promising results in other tasks, e.g., video denoising. 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. Although adopting SSIM alone can produce the highest MS-SSIM score, but the combination of L1 loss and SSIM loss can achieve the best trade-off between the PSNR performance and MS-SSIM score. However, the redundant parameters within the network structure can cause. Without any special denoisng design, NeRV outperforms traditional hand-crafted denoising algorithms (medium filter etc.) NeRV: Neural Reflectance and Visibility Fields for Relighting and View SynthesisAuthors: Pratul P. Srinivasan, Boyang Deng, Xiuming Zhang, Matthew Tancik, Be. Due to the simple decoding process (feedforward operation), NeRV shows great advantage, even for carefully-optimized H.264. Input embedding. methods are restricted by a long and complex pipeline, specifically designed PS-NeRV, which represents videos as a function of patches and the corresponding patch coordinate. At similar memory budget, NeRV shows image details with better quality. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. 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 . Besides compression, we demonstrate the generalization of NeRV for video denoising. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. Bits-per-pixel (BPP) is adopted to indicate the compression ratio. We perform experiments on Big Buck Bunny sequence from scikit-video to compare our NeRV with pixel-wise implicit representations, which has 132 frames of 7201080 resolution. proposed an effective image compression approach and generalized it into video compression by adding interpolation loop modules. And lots of speepup can be expected by running quantizaed model on special hardware. 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. As the first image-wise neural representation, NeRV generally achieves comparable performance with traditional video compression techniques and other learning-based video compression approaches. Given a frame index, NeRV outputs the corresponding RGB image.. 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 . When compare with state-of-the-arts, we run the model for 1500 epochs, with batchsize of 6. videos as frame sequences, we represent videos as neural networks taking frame The compression performance is quite robust to NeRV models of different sizes, and each step shows consistent contribution to our final results. Compared to pixel-wise implicit representation, NeRV output the whole image and shows great efficiency, improving the encoding speed by 25 to 70, the decoding speed by 38 to 132, while achieving better video quality. Given a frame index, NeRV outputs the corresponding RGB image. We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. Given a frame index, NeRV outputs the corresponding RGB image. Activation layer. Note that HEVC is run on CPU, while all other learning-based methods are run on a single GPU, including our NeRV. Spatial-Temporal Context, MiNL: Micro-images based Neural Representation for Light Fields, Streaming Multiscale Deep Equilibrium Models, A Real-time Action Representation with Temporal Encoding and Deep Therefore, video encoding is done by fitting a neural network f to a given video, such that it can map each input timestamp to the corresponding RGB frame. We propose a novel neural representation for videos (NeRV) which encodes for the task. The source code and pre-trained model can be found at ( A) Example spatial rate maps for excitatory neurons from posterior, intermediate, or anterior hippocampus, plotted as in Fig. While some recent works have tried to directly reconstruct the whole image with CNNs. Add a Enter your feedback below and we'll get back to you as soon as possible. Acknowledge Based on the magnitude of weight values, we set weights below a threshold as zero. When BPP becomes large, the performance gap is mostly because of the lack of full training due to GPU resources limitations. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. 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.
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