Besides compression, we demonstrate the generalization of NeRV for video denoising. Specifically, with a fairly simple deep neural network design, NeRV can reconstruct the corresponding video frames with high quality, given the frame index. The source code and pre-trained model can be found at De Fauw, and K. Kavukcuoglu, A guide to convolution arithmetic for deep learning, E. Dupont, A. Goliski, M. Alizadeh, Y. W. Teh, and A. Doucet, COIN: compression with implicit neural representations, F. Faghri, I. Tabrizian, I. Markov, D. Alistarh, D. Roy, and A. Ramezani-Kebrya, Adaptive gradient quantization for data-parallel sgd, K. Genova, F. Cole, A. Sud, A. Sarna, and T. A. Funkhouser, K. Genova, F. Cole, D. Vlasic, A. Sarna, W. T. Freeman, and T. Funkhouser, Learning shape templates with structured implicit functions, Proceedings of the IEEE/CVF International Conference on Computer Vision, S. Gupta, A. Agrawal, K. Gopalakrishnan, and P. Narayanan, Deep learning with limited numerical precision, Deep compression: compressing deep neural networks with pruning, trained quantization and huffman coding, Distilling the knowledge in a neural network, Multilayer feedforward networks are universal approximators, A method for the construction of minimum-redundancy codes, B. Jacob, S. Kligys, B. Chen, M. Zhu, M. Tang, A. Howard, H. Adam, and D. Kalenichenko, Quantization and training of neural networks for efficient integer-arithmetic-only inference, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, M. Jaderberg, A. Vedaldi, and A. Zisserman, Speeding up convolutional neural networks with low rank expansions. Sampling efficiency of NeRV also simplify the optimization problem, which leads to better reconstruction quality compared to pixel-wise representations. Besides video compression, we also explore other applications of the NeRV representation for the video denoising task. 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. Conclusion. As a normal practice, we fine-tune the model to regain the representation, after the pruning operation. We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. Table4.5 shows results for common activation layers. In Table4.5, we apply common normalization layers in NeRV block. We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. several video-related tasks. Classical INRs methods generally utilize MLPs to map input coordinates to output pixels. While classic approaches have largely relied on discrete representations such as textured meshes [16, 53] The code is organized as follows: train_nerv.py includes a generic traiing routine. Video encoding in NeRV is simply fitting a neural network to video frames and decoding process is a simple feedforward operation. 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 . Given a frame index, NeRV outputs the corresponding RGB image.. Figure6 shows the results of different pruning ratios, where model of 40% sparsity still reach comparable performance with the full model. Fig. 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. 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. We apply several common noise patterns on the original video and train the model on the perturbed ones. videos in neural networks. We gratefully acknowledge the support of the OpenReview Sponsors. In Table6, PE means positional encoding as in Equation1, which greatly improves the baseline, None means taking the frame index as input directly. Given a frame index, NeRV outputs the corresponding RGB image. Model Quantization. The GELU[19]activation function achieve the highest performances, which is adopted as our default design. Activation layer. Open Access. PS-NeRV, which represents videos as a function of patches and the corresponding patch coordinate. What is a video? Given a neural network fit on a video, we use global unstructured pruning to reduce the model size first. Video compression visulization. As the most popular media format nowadays, videos are generally viewed as frames of sequences. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. data/ directory video/imae dataset, we provide big buck bunny here. Unlike conventional representations that treat Feeling: not all feelings include emotion, such as the feeling of knowing.In the context of emotion, feelings are best understood as a subjective representation of emotions, private to the individual experiencing them. Video compression visualization. task. 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. Following prior works, we used ffmpeg[49]. Most notably, we examine the suitability of NeRV for video compression. We would like to show you a description here but the site won't allow us. for the task. 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. 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. The main differences between our work and image-wise implicit representation are the output space and architecture designs. A convolutional neural network (CNN) is a type of artificial neural network used in image recognition and processing that is specifically designed to process pixel data.. CNNs are powerful image processing, artificial intelligence that use deep learning to perform both generative and descriptive tasks, often using machine vison that includes . The zoomed areas show that our model produces fewer artifacts and the output is smoother. For example, conventional video compression methods are restricted by a long and complex pipeline, specifically designed for the task. ), S. Peng, M. Niemeyer, L. Mescheder, M. Pollefeys, and A. Geiger, Model compression via distillation and quantization, N. Rahaman, A. Baratin, D. Arpit, F. Draxler, M. Lin, F. Hamprecht, Y. Bengio, and A. Courville, International Conference on Machine Learning, R. Rigamonti, A. Sironi, V. Lepetit, and P. Fua, Proceedings of the IEEE conference on computer vision and pattern recognition, O. Rippel, S. Nair, C. Lew, S. Branson, A. G. Anderson, and L. Bourdev, W. Shang, K. Sohn, D. Almeida, and H. Lee, international conference on machine learning, W. Shi, J. Caballero, F. Huszr, J. Totz, A. P. 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Kaiser, and I. Polosukhin, The jpeg still picture compression standard, IEEE transactions on consumer electronics, H. Wang, W. Gan, S. Hu, J. Y. Lin, L. Jin, L. Song, P. Wang, I. Katsavounidis, A. Aaron, and C. J. Kuo, MCL-jcv: a jnd-based h. 264/avc video quality assessment dataset, 2016 IEEE International Conference on Image Processing (ICIP), N. Wang, J. Choi, D. Brand, C. Chen, and K. Gopalakrishnan, Training deep neural networks with 8-bit floating point numbers, Z. Wang, E. P. Simoncelli, and A. C. Bovik, Multiscale structural similarity for image quality assessment, The Thrity-Seventh Asilomar Conference on Signals, Systems Computers, 2003, W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li, Learning structured sparsity in deep neural networks, T. Wiegand, G. J. Sullivan, G. Bjontegaard, and A. Luthra, Overview of the h. 264/avc video coding standard, Video compression through image interpolation, Proceedings of the European Conference on Computer Vision (ECCV), R. Yang, F. Mentzer, L. V. Gool, and R. Timofte, Learning for video compression with hierarchical quality and recurrent enhancement, R. Yang, Y. Yang, J. Marino, and S. Mandt. Such a long pipeline makes the decoding process very complex as well. Loss objective. 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. 2 Spatial representations are organized along the long axis of the hippocampus. 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). 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 to 132x, while achieving better video quality. Given a frame index, NeRV outputs the corresponding RGB image. videos as frame sequences, we represent videos as neural networks taking frame In contrast, with NeRV, we can use any neural network compression 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. A schematic interpretation of this is a curve in 2D space, where each point can be characterized with a (x,y) pair representing the spatial state. The source code and pre-trained model can be found at https://github.com/haochen-rye/NeRV.git. 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). Due to the simple decoding process (feedforward operation), NeRV shows great advantage, even for carefully-optimized H.264. As the first image-wise neural representation, NeRV generally achieves comparable performance with traditional video compression techniques and other learning-based video compression approaches. 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 first conduct ablation study on video Big Buck Bunny. DIP emphasizes that its image prior is only captured by the network structure of Convolution operations because it only feeds on a single image. 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. However, neither pixel-wise nor image-wise representation is the most suitable strategy for video data. Rate distortion plots on the MCL-JCV dataset. For fine-tune process after pruning, we use 50 epochs for both UVG and Big Buck Bunny. As the most popular media format nowadays, videos are generally viewed as frames of sequences. These can be viewed as denoising upper bound for any additional compression process. Input Embedding. Add a To compare with state-of-the-arts methods on video compression task, we do experiments on the widely used UVG[32], consisting of 7 videos and 3900 frames with 19201080 in total. Figure6 shows the full compression pipeline with NeRV. 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. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. 2022 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. At similar memory budget, NeRV shows image details with better quality. model_nerv.py contains the dataloader and neural network architecure. The code is organized as follows: train_nerv.py includes a generic traiing routine. to produce the evaluation metrics for H.264 and HEVC. As an image-wise implicit representation, NeRV output the whole image and . 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. By changing the hidden dimension of MLP and channel dimension of NeRV blocks, we can build NeRV model with different sizes. In this work, we present a novel neural representation for videos, NeRV, which encodes videos into neural networks. Given a frame index, NeRV outputs the corresponding RGB image. 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. Enter your feedback below and we'll get back to you as soon as possible. In UVG experiments on video compression task, we train models with different sizes by changing the value of C1,C2 to (48,384), (64,512), (128,512), (128,768), (128,1024), (192,1536), and (256,2048). 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. 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. We shows performance results of different combinations of L2, L1, and SSIM loss. Papers With Code is a free resource with all data licensed under. Do not remove: This comment is monitored to verify that the site is working properly, Advances in Neural Information Processing Systems 34 (NeurIPS 2021). (c) and (e) are denoising output for DIP, Input embedding ablation. As shown in Table3, the decoding video quality keeps increasing when the training epochs are longer. Compression ablation. There are some limitations with the proposed NeRV. Compare with state-of-the-arts methods. 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. representation, NeRV output the whole image and shows great efficiency compared 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. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. A novel neural representation for videos (NeRV) which encodes videos in neural networks taking frame index as input, which can be used as a proxy for video compression, and achieve comparable performance to traditional frame-based video compression approaches. NeRV [5,6], RGBNeRV2 T H W T H W NeRVT NeRV NeRVMLP+ConvNetsMLPRGB NeRV Use the "Report an Issue" link to request a name change. Entropy Encoding. Given a frame index, NeRV outputs the corresponding RGB image. decoding process is a simple feedforward operation. Acknowledge Given a frame index, NeRV outputs the corresponding RGB image. We also show that NeRV can outperform standard denoising methods. 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. 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. Temporal interpolation results for video with small motion. Surprisingly, our model tries to avoid the influence of the noise and regularizes them implicitly with little harm to the compression task simultaneously, which can serve well for most partially distorted videos in practice. Acknowledgement. Our method represents the scene as a continuous volumetric function parameterized as MLPs whose inputs are a 3D . With such a representation, we can treat videos as neural networks, simplifying several video-related tasks. We speedup NeRV by running it in half precision (FP16). On the contrary, our NeRV can output frames at any random time index independently, thus making parallel decoding much simpler. This project was partially funded by the DARPA SAIL-ON (W911NF2020009) program, an independent grant from Facebook AI, and Amazon Research Award to AS. Pixel-wise representations output the RGB value for each pixel, while NeRV outputs a whole image, demonstrated in Figure2. PSNR and MS-SSIM are adopted for evaluation of the reconstructed videos. The source code and pre-trained model can be found at https://github.com/haochen-rye/NeRV.git. We test a smaller model on Bosphorus video, and it also has a better performance compared to H.265 codec with similar BPP. 36 PDF Decomposing Motion and Content for Natural Video Sequence Prediction The difference is calculated by the L1 loss (absolute value, scaled by the same level for the same frame, and the darker the more different). 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. 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.
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