Predictive Coding For Animation-Based Video Compression

• URL: http://arxiv.org/abs/2307.04187v1
• Date: Sun, 9 Jul 2023 14:40:54 GMT
• Title: Predictive Coding For Animation-Based Video Compression
• Authors: Goluck Konuko, St\'ephane Lathuili\`ere and Giuseppe Valenzise
• Abstract summary: We propose a predictive coding scheme which uses image animation as a predictor, and codes the residual with respect to the actual target frame. Our experiments indicate a significant gain, in excess of 70% compared to the HEVC video standard and over 30% compared to VVC.
• Score: 13.161311799049978
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We address the problem of efficiently compressing video for conferencing-type applications. We build on recent approaches based on image animation, which can achieve good reconstruction quality at very low bitrate by representing face motions with a compact set of sparse keypoints. However, these methods encode video in a frame-by-frame fashion, i.e. each frame is reconstructed from a reference frame, which limits the reconstruction quality when the bandwidth is larger. Instead, we propose a predictive coding scheme which uses image animation as a predictor, and codes the residual with respect to the actual target frame. The residuals can be in turn coded in a predictive manner, thus removing efficiently temporal dependencies. Our experiments indicate a significant bitrate gain, in excess of 70% compared to the HEVC video standard and over 30% compared to VVC, on a datasetof talking-head videos

Related papers

• Accelerating Learned Video Compression via Low-Resolution Representation Learning [18.399027308582596]
  We introduce an efficiency-optimized framework for learned video compression that focuses on low-resolution representation learning. Our method achieves performance levels on par with the low-decay P configuration of the H.266 reference software VTM.
  arXiv  Detail & Related papers  (2024-07-23T12:02:57Z)
• Blurry Video Compression: A Trade-off between Visual Enhancement and Data Compression [65.8148169700705]
  Existing video compression (VC) methods primarily aim to reduce the spatial and temporal redundancies between consecutive frames in a video. Previous works have achieved remarkable results on videos acquired under specific settings such as instant (known) exposure time and shutter speed. In this work, we tackle the VC problem in a general scenario where a given video can be blurry due to predefined camera settings or dynamics in the scene.
  arXiv  Detail & Related papers  (2023-11-08T02:17:54Z)
• Perceptual Quality Improvement in Videoconferencing using Keyframes-based GAN [28.773037051085318]
  We propose a novel GAN-based method for compression artifacts reduction in videoconferencing. First, we extract multi-scale features from the compressed and reference frames. Then, our architecture combines these features in a progressive manner according to facial landmarks.
  arXiv  Detail & Related papers  (2023-11-07T16:38:23Z)
• IBVC: Interpolation-driven B-frame Video Compression [68.18440522300536]
  B-frame video compression aims to adopt bi-directional motion estimation and motion compensation (MEMC) coding for middle frame reconstruction. Previous learned approaches often directly extend neural P-frame codecs to B-frame relying on bi-directional optical-flow estimation. We propose a simple yet effective structure called Interpolation-B-frame Video Compression (IBVC) to address these issues.
  arXiv  Detail & Related papers  (2023-09-25T02:45:51Z)
• VNVC: A Versatile Neural Video Coding Framework for Efficient Human-Machine Vision [59.632286735304156]
  It is more efficient to enhance/analyze the coded representations directly without decoding them into pixels. We propose a versatile neural video coding (VNVC) framework, which targets learning compact representations to support both reconstruction and direct enhancement/analysis.
  arXiv  Detail & Related papers  (2023-06-19T03:04:57Z)
• Compressing Video Calls using Synthetic Talking Heads [43.71577046989023]
  We propose an end-to-end system for talking head video compression. Our algorithm transmits pivot frames intermittently while the rest of the talking head video is generated by animating them. We use a state-of-the-art face reenactment network to detect key points in the non-pivot frames and transmit them to the receiver.
  arXiv  Detail & Related papers  (2022-10-07T16:52:40Z)
• Deep Contextual Video Compression [20.301569390401102]
  We propose a deep contextual video compression framework to enable a paradigm shift from predictive coding to conditional coding. Our method can significantly outperform the previous state-of-theart (SOTA) deep video compression methods.
  arXiv  Detail & Related papers  (2021-09-30T12:14:24Z)
• Conditional Entropy Coding for Efficient Video Compression [82.35389813794372]
  We propose a very simple and efficient video compression framework that only focuses on modeling the conditional entropy between frames. We first show that a simple architecture modeling the entropy between the image latent codes is as competitive as other neural video compression works and video codecs. We then propose a novel internal learning extension on top of this architecture that brings an additional 10% savings without trading off decoding speed.
  arXiv  Detail & Related papers  (2020-08-20T20:01:59Z)
• M-LVC: Multiple Frames Prediction for Learned Video Compression [111.50760486258993]
  We propose an end-to-end learned video compression scheme for low-latency scenarios. In our scheme, the motion vector (MV) field is calculated between the current frame and the previous one. Experimental results show that the proposed method outperforms the existing learned video compression methods for low-latency mode.
  arXiv  Detail & Related papers  (2020-04-21T20:42:02Z)
• Content Adaptive and Error Propagation Aware Deep Video Compression [110.31693187153084]
  We propose a content adaptive and error propagation aware video compression system. Our method employs a joint training strategy by considering the compression performance of multiple consecutive frames instead of a single frame. Instead of using the hand-crafted coding modes in the traditional compression systems, we design an online encoder updating scheme in our system.
  arXiv  Detail & Related papers  (2020-03-25T09:04:24Z)

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.