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Input Embedding. NIPS 2021NeRV: Neural Representations for Videos 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. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, International conference on machine learning, Exploiting cyclic symmetry in convolutional neural networks, Sgdr: stochastic gradient descent with warm restarts, Understanding and improving convolutional neural networks via concatenated rectified linear units, Hierarchical autoregressive modeling for neural video compression. Video encoding in NeRV is simply fitting a neural network to video frames and decoding process is a simple feedforward operation. Given a frame index, NeRV outputs the corresponding RGB image.. Training speed means time/epoch, while encoding time is the total training time. Video compression visulization. 21 May 2021, 20:48 (modified: 22 Jan 2022, 15:59), neural representation, implicit representation, video compression, video denoising. When compare with state-of-the-arts, we run the model for 1500 epochs, with batchsize of 6. Given these intuitions, we propose NeRV, a novel representation that represents videos as implicit functions and encodes them into neural networks. Loss objective. Video encoding in NeRV is simply fitting a neural network to video frames and decoding process is a simple feedforward operation. As we show in Fig12, NeRV can give quite reasonable predictions on the unseen frame, which has good and comparable visual quality compared to the adjacent seen frames. to produce the evaluation metrics for H.264 and HEVC. In contrast, NeRV [ 2] is proposed as an image-wise representation method, which represents a video as a function of time. , which are well-engineered and tuned to be fast and efficient. ), and reach comparable bit-distortion performance with other methods. For ablation study on UVG, we use cosine annealing learning rate schedule[30]. NeRV: Neural Representations for Videos - GitHub Pages Leveraging MLPs to directly output all pixel values of the frames can lead to huge parameters, especially when the images resolutions are large. Given an input timestamp t, normalized between (0,1], the output of embedding function () is then fed to the following neural network. The GELU[19]activation function achieve the highest performances, which is adopted as our default design. Abstract and Figures We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. The source code and pre-trained model can be found at https://github.com/haochen-rye/NeRV.git. Specifically, we use model pruning and quantization to reduce the model size without significantly deteriorating the performance. Open Access. Do not remove: This comment is monitored to verify that the site is working properly, Advances in Neural Information Processing Systems 34 (NeurIPS 2021). NeRV: Neural Representations for Videos - NASA/ADS The results are compared with some standard denoising methods including Gaussian, uniform, and median filtering. PS-NeRV: Patch-wise Stylized Neural Representations for Videos Video encoding in NeRV is simply fitting a neural network to video frames and decoding . videos in neural networks. 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. Before the resurgence of deep networks, handcrafted image compression techniques, like JPEG. Similar findings can be found in [33], without any input embedding, the model can not learn high-frequency information, resulting in much lower performance. Compare with state-of-the-arts methods. Figure6 shows the results of different pruning ratios, where model of 40% sparsity still reach comparable performance with the full model. Neural representations of space in the hippocampus of a food - Science For example, conventional video compression methods are restricted by a long and complex pipeline, specifically designed for the task. Given a frame index, NeRV outputs the corresponding RGB image. Hao Chen - GitHub Pages With such a representation, we can treat videos as neural networks, simplifying We hypothesize that the normalization layer reduces the over-fitting capability of the neural network, which is contradictory to our training objective. While some recent works have tried to directly reconstruct the whole image with CNNs. We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. Without any special denoisng design, NeRV outperforms traditional hand-crafted denoising algorithms (medium filter etc.) (c) and (e) are denoising output for DIP, Input embedding ablation. Current research on model compression research can be divided into four groups: parameter pruning and quantization[51, 17, 18, 57, 23, 27]; low-rank factorization[40, 10, 24]; transferred and compact convolutional filters[9, 62, 42, 11]; and knowledge distillation[4, 20, 7, 38]. Please note that although Transpose convolution[12] reach comparable results, it greatly slowdown the training speed compared to the PixelShuffle. We also compare NeRV with another neural-network-based denoising method, Deep Image Prior (DIP) [50]. Following prior works, we used ffmpeg[49]. 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. log files (tensorboard, txt, state_dict etc . and ConvNets-based denoisng methods. While classic approaches have largely relied on discrete representations such as textured meshes [16, 53] Classical INRs methods generally utilize MLPs to map input coordinates to output pixels. The way illumination is represented varies drastically between the methods. At similar BPP, NeRV reconstructs videos with better details. E-NeRV: Expedite Neural Video Representation with Disentangled Spatial Abstract We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. 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. Neural-PIL: Neural Pre-Integrated Lighting for Reflectance DIP emphasizes that its image prior is only captured by the network structure of Convolution operations because it only feeds on a single image. Model Compression. Our method represents the scene as a continuous volumetric function parameterized as MLPs whose inputs are a 3D . NeRV: Neural Representations for Videos-pudn.com We study how to represent a video with implicit neural representations (INRs). For fair comparison, we train SIREN and FFN for 120 epochs to make encoding time comparable. PS-NeRV: Patch-wise Stylized Neural Representations for Videos, E-NeRV: Expedite Neural Video Representation with Disentangled Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. PDF NeRV: Neural Representations for Videos 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. 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. HNeRV: A Hybrid Neural Representation for Videos | OpenReview The compression performance is quite robust to NeRV models of different sizes, and each step shows consistent contribution to our final results. where q is the q percentile value for all parameters in . In Table4.5, we apply common normalization layers in NeRV block. It is designed for production environments and is optimized for speed and accuracy on a small number of training images. We train a pixel-wise implicit representation to fit video frames. videos as frame sequences, we represent videos as neural networks taking frame 2 Spatial representations are organized along the long axis of the hippocampus. NeRV: Neural Reflectance and Visibility Fields for Relighting - YouTube Hao Chen, Bo He, Hanyu Wang, Yixuan Ren, Ser-Nam Lim], Abhinav Shrivastava This is the official implementation of the paper "NeRV: Neural Representations for Videos ". to pixel-wise implicit representation, improving the encoding speed by 25x to methods are restricted by a long and complex pipeline, specifically designed 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. Implicit Neural Representation. We show loss objective ablation in Table10. 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]. Model Pruning. We propose a image-wise neural representation (NeRV) to encodes videos in neural networks, which takes frame index as input and outputs the corresponding RGB image. Due to the simple decoding process (feedforward operation), NeRV shows great advantage, even for carefully-optimized H.264. Note that HEVC is run on CPU, while all other learning-based methods are run on a single GPU, including our NeRV. Typically, a video captures a dynamic visual scene using a sequence of frames. And we change the filter width to build NeRV model of comparable sizes, named as NeRV-S, NeRV-M, and NeRV-L. November 1, 2021 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. The overhead to store scale and min can be ignored given the large parameter number of , e.g., they account for only 0.005% in a small 33 Conv with 64 input channels and 64 output channels (37k parameters in total). 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. We test a smaller model on Bosphorus video, and it also has a better performance compared to H.265 codec with similar BPP. Official Pytorch implementation for video neural representation (NeRV) Unlike conventional representations that treat Compare with pixel-wise implicit representations. 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. In this paper, we propose E-NeRV, which dramatically expedites NeRV by decomposing the image-wise implicit neural representation into separate spatial and temporal context. The key idea is to represent an object as a function approximated via a neural network, which maps the coordinate to its corresponding value (e.g., pixel coordinate for an image and RGB value of the pixel). 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. 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. PS-NeRV: Patch-wise Stylized Neural Representations for Videos We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. 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). Our model compression composes of four standard sequential steps: video overfit, model pruning, weight quantization, and weight encoding as shown in Figure3. 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. [better source needed] NeRV: Neural Representations for Videos - arXiv Vanity Abstract:We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. [PDF] E-NeRV: Expedite Neural Video Representation with Disentangled Given a frame index, NeRV outputs the corresponding RGB image. By mapping the inputs to a high embedding space, the neural network can better fit data with high-frequency variations. ), 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. Aitken, R. Bishop, D. Rueckert, and Z. Wang, Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network, V. Sitzmann, J. Martel, A. Bergman, D. Lindell, and G. Wetzstein, Implicit neural representations with periodic activation functions, Advances in Neural Information Processing Systems, V. Sitzmann, M. Zollhfer, and G. Wetzstein, A. Skodras, C. Christopoulos, and T. Ebrahimi, The jpeg 2000 still image compression standard, G. J. Sullivan, J. Ohm, W. Han, and T. Wiegand, Overview of the high efficiency video coding (hevc) standard, IEEE Transactions on circuits and systems for video technology, M. Tancik, P. P. Srinivasan, B. Mildenhall, S. Fridovich-Keil, N. Raghavan, U. Singhal, R. Ramamoorthi, J. T. Barron, and R. Ng, Fourier features let networks learn high frequency functions in low dimensional domains, Improving the speed of neural networks on cpus, A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. 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. Although explicit representations outperform implicit ones in encoding speed and compression ratio now, NeRV shows great advantage in decoding speed. Although it is not yet competitive with the state-of-the-art compression methods, it shows promising and attractive proprieties. With such a representation, we can treat videos as neural networks, simplifying several video-related tasks. As a fundamental task of computer vision and image processing, visual data compression has been studied for several decades. We provide the experiment results for video compression task on MCL-JCL[54]dataset in Figure11 and Figure11. NeRV takes the time embedding as input and outputs the corresponding RGB Frame. OpenReview is a long-term project to advance science through improved peer review, with legal nonprofit status through Code for Science & Society. 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. Video encoding in NeRV is simply fitting a neural network to video frames and We compare with H.264[58], HEVC[47], STAT-SSF-SP[61], HLVC[60], Scale-space[1], and Wu et al. Since Huffman Coding is lossless, it is guaranteed that a decent compression can be achieved without any impact on the reconstruction quality. 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 . Given a parameter tensor. 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. 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. Papers With Code is a free resource with all data licensed under. 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 Emotion can be differentiated from a number of similar constructs within the field of affective neuroscience:. Comparing to explicit 3D representations, such as voxel, point cloud, and mesh, the continuous implicit neural representation can compactly encode high-resolution signals in a memory-efficient way. 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). We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. Comparison of different video representations. However, the redundant parameters within the network structure can cause a large model size when scaling up for desirable performance. As an image-wise implicit representation, NeRV output the whole image and . Given a frame index, NeRV outputs the corresponding RGB image. Finally, more advanced and cutting the edge model compression methods can be applied to NeRV and obtain higher compression ratios. For example, conventional video compression methods are restricted by a long and complex pipeline, specifically designed for the 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. Specifically, in NeRV, we use Positional Encoding[33, 52, 48] as our embedding function. where b and l are hyper-parameters of the networks. We implement our model in PyTorch, We compare NeRV with pixel-wise implicit representations on Big Buck Bunny video. Add a E-NeRV: Expedite Neural Video Representation with - CatalyzeX We conduct extensive experiments on popular video compression datasets, such as UVG. With similar model sizes, PixelShuffle shows best results. For experiments on Big Buck Bunny, we train NeRV for 1200 epochs unless otherwise denoted. We study how to represent a video with implicit neural representations (INRs). Given a frame index, NeRV outputs the corresponding RGB image. 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.
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