Offline and Online Optical Flow Enhancement for Deep Video Compression

• URL: http://arxiv.org/abs/2307.05092v1
• Date: Tue, 11 Jul 2023 07:52:06 GMT
• Title: Offline and Online Optical Flow Enhancement for Deep Video Compression
• Authors: Chuanbo Tang, Xihua Sheng, Zhuoyuan Li, Haotian Zhang, Li Li, Dong Liu
• Abstract summary: Motion information is represented as optical flows in most of the existing deep video compression networks. We conduct experiments on a state-of-the-art deep video compression scheme, DCVC.
• Score: 14.445058335559994
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video compression relies heavily on exploiting the temporal redundancy between video frames, which is usually achieved by estimating and using the motion information. The motion information is represented as optical flows in most of the existing deep video compression networks. Indeed, these networks often adopt pre-trained optical flow estimation networks for motion estimation. The optical flows, however, may be less suitable for video compression due to the following two factors. First, the optical flow estimation networks were trained to perform inter-frame prediction as accurately as possible, but the optical flows themselves may cost too many bits to encode. Second, the optical flow estimation networks were trained on synthetic data, and may not generalize well enough to real-world videos. We address the twofold limitations by enhancing the optical flows in two stages: offline and online. In the offline stage, we fine-tune a trained optical flow estimation network with the motion information provided by a traditional (non-deep) video compression scheme, e.g. H.266/VVC, as we believe the motion information of H.266/VVC achieves a better rate-distortion trade-off. In the online stage, we further optimize the latent features of the optical flows with a gradient descent-based algorithm for the video to be compressed, so as to enhance the adaptivity of the optical flows. We conduct experiments on a state-of-the-art deep video compression scheme, DCVC. Experimental results demonstrate that the proposed offline and online enhancement together achieves on average 12.8% bitrate saving on the tested videos, without increasing the model or computational complexity of the decoder side.

Related papers

• Binarized Low-light Raw Video Enhancement [49.65466843856074]
  Deep neural networks have achieved excellent performance on low-light raw video enhancement. In this paper, we explore the feasibility of applying the extremely compact binary neural network (BNN) to low-light raw video enhancement.
  arXiv  Detail & Related papers  (2024-03-29T02:55:07Z)
• Breaking of brightness consistency in optical flow with a lightweight CNN network [7.601414191389451]
  In this work, a lightweight network is used to extract robust convolutional features and corners with strong invariance. Modifying the typical brightness consistency of the optical flow method to the convolutional feature consistency yields the light-robust hybrid optical flow method. A more accurate visual inertial system is constructed by replacing the optical flow method in VINS-Mono.
  arXiv  Detail & Related papers  (2023-10-24T09:10:43Z)
• UFD-PRiME: Unsupervised Joint Learning of Optical Flow and Stereo Depth through Pixel-Level Rigid Motion Estimation [4.445751695675388]
  Both optical flow and stereo disparities are image matches and can therefore benefit from joint training. We design a first network that estimates flow and disparity jointly and is trained without supervision. A second network, trained with optical flow from the first as pseudo-labels, takes disparities from the first network, estimates 3D rigid motion at every pixel, and reconstructs optical flow again.
  arXiv  Detail & Related papers  (2023-10-07T07:08:25Z)
• MVFlow: Deep Optical Flow Estimation of Compressed Videos with Motion Vector Prior [16.633665275166706]
  We propose an optical flow model, MVFlow, which uses motion vectors to improve the speed and accuracy of optical flow estimation for compressed videos. The experimental results demonstrate the superiority of our proposed MVFlow by 1.09 compared to existing models or save time to achieve similar accuracy to existing models.
  arXiv  Detail & Related papers  (2023-08-03T07:16:18Z)
• A Coding Framework and Benchmark towards Low-Bitrate Video Understanding [63.05385140193666]
  We propose a traditional-neural mixed coding framework that takes advantage of both traditional codecs and neural networks (NNs) The framework is optimized by ensuring that a transportation-efficient semantic representation of the video is preserved. We build a low-bitrate video understanding benchmark with three downstream tasks on eight datasets, demonstrating the notable superiority of our approach.
  arXiv  Detail & Related papers  (2022-02-06T16:29:15Z)
• Dual-view Snapshot Compressive Imaging via Optical Flow Aided Recurrent Neural Network [14.796204921975733]
  Dual-view snapshot compressive imaging (SCI) aims to capture videos from two field-of-views (FoVs) in a single snapshot. It is challenging for existing model-based decoding algorithms to reconstruct each individual scene. We propose an optical flow-aided recurrent neural network for dual video SCI systems, which provides high-quality decoding in seconds.
  arXiv  Detail & Related papers  (2021-09-11T14:24:44Z)
• Learning optical flow from still images [53.295332513139925]
  We introduce a framework to generate accurate ground-truth optical flow annotations quickly and in large amounts from any readily available single real picture. We virtually move the camera in the reconstructed environment with known motion vectors and rotation angles. When trained with our data, state-of-the-art optical flow networks achieve superior generalization to unseen real data.
  arXiv  Detail & Related papers  (2021-04-08T17:59:58Z)
• FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation [97.99012124785177]
  FLAVR is a flexible and efficient architecture that uses 3D space-time convolutions to enable end-to-end learning and inference for video framesupervised. We demonstrate that FLAVR can serve as a useful self- pretext task for action recognition, optical flow estimation, and motion magnification.
  arXiv  Detail & Related papers  (2020-12-15T18:59:30Z)
• Ultra-low bitrate video conferencing using deep image animation [7.263312285502382]
  We propose a novel deep learning approach for ultra-low video compression for video conferencing applications. We employ deep neural networks to encode motion information as keypoint displacement and reconstruct the video signal at the decoder side.
  arXiv  Detail & Related papers  (2020-12-01T09:06:34Z)
• Optical Flow Distillation: Towards Efficient and Stable Video Style Transfer [67.36785832888614]
  This paper proposes to learn a lightweight video style transfer network via knowledge distillation paradigm. We adopt two teacher networks, one of which takes optical flow during inference while the other does not. The output difference between these two teacher networks highlights the improvements made by optical flow, which is then adopted to distill the target student network.
  arXiv  Detail & Related papers  (2020-07-10T03:00:33Z)
• Cascaded Deep Video Deblurring Using Temporal Sharpness Prior [88.98348546566675]
  The proposed algorithm mainly consists of optical flow estimation from intermediate latent frames and latent frame restoration steps. It first develops a deep CNN model to estimate optical flow from intermediate latent frames and then restores the latent frames based on the estimated optical flow. We show that exploring the domain knowledge of video deblurring is able to make the deep CNN model more compact and efficient.
  arXiv  Detail & Related papers  (2020-04-06T09:13:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.