Video Coding Using Learned Latent GAN Compression

• URL: http://arxiv.org/abs/2207.04324v2
• Date: Tue, 12 Jul 2022 21:17:39 GMT
• Title: Video Coding Using Learned Latent GAN Compression
• Authors: Mustafa Shukor, Bharath Bhushan Damodaran, Xu Yao, Pierre Hellier
• Abstract summary: We leverage the generative capacity of GANs such as StyleGAN to represent and compress a video. Each frame is inverted in the latent space of StyleGAN, from which the optimal compression is learned.
• Score: 1.6058099298620423
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose in this paper a new paradigm for facial video compression. We leverage the generative capacity of GANs such as StyleGAN to represent and compress a video, including intra and inter compression. Each frame is inverted in the latent space of StyleGAN, from which the optimal compression is learned. To do so, a diffeomorphic latent representation is learned using a normalizing flows model, where an entropy model can be optimized for image coding. In addition, we propose a new perceptual loss that is more efficient than other counterparts. Finally, an entropy model for video inter coding with residual is also learned in the previously constructed latent representation. Our method (SGANC) is simple, faster to train, and achieves better results for image and video coding compared to state-of-the-art codecs such as VTM, AV1, and recent deep learning techniques. In particular, it drastically minimizes perceptual distortion at low bit rates.

Related papers

• Progressive Learning with Visual Prompt Tuning for Variable-Rate Image Compression [60.689646881479064]
  We propose a progressive learning paradigm for transformer-based variable-rate image compression. Inspired by visual prompt tuning, we use LPM to extract prompts for input images and hidden features at the encoder side and decoder side, respectively. Our model outperforms all current variable image methods in terms of rate-distortion performance and approaches the state-of-the-art fixed image compression methods trained from scratch.
  arXiv  Detail & Related papers  (2023-11-23T08:29:32Z)
• Scene Matters: Model-based Deep Video Compression [13.329074811293292]
  We propose a model-based video compression (MVC) framework that regards scenes as the fundamental units for video sequences. Our proposed MVC directly models novel intensity variation of the entire video sequence in one scene, seeking non-redundant representations instead of reducing redundancy. Our method achieves up to a 20% reduction compared to the latest video standard H.266 and is more efficient in decoding than existing video coding strategies.
  arXiv  Detail & Related papers  (2023-03-08T13:15:19Z)
• Reducing The Amortization Gap of Entropy Bottleneck In End-to-End Image Compression [2.1485350418225244]
  End-to-end deep trainable models are about to exceed the performance of the traditional handcrafted compression techniques on videos and images. We propose a simple yet efficient instance-based parameterization method to reduce this amortization gap at a minor cost.
  arXiv  Detail & Related papers  (2022-09-02T11:43:45Z)
• The Devil Is in the Details: Window-based Attention for Image Compression [58.1577742463617]
  Most existing learned image compression models are based on Convolutional Neural Networks (CNNs) In this paper, we study the effects of multiple kinds of attention mechanisms for local features learning, then introduce a more straightforward yet effective window-based local attention block. The proposed window-based attention is very flexible which could work as a plug-and-play component to enhance CNN and Transformer models.
  arXiv  Detail & Related papers  (2022-03-16T07:55:49Z)
• Neural Data-Dependent Transform for Learned Image Compression [72.86505042102155]
  We build a neural data-dependent transform and introduce a continuous online mode decision mechanism to jointly optimize the coding efficiency for each individual image. The experimental results show the effectiveness of the proposed neural-syntax design and the continuous online mode decision mechanism.
  arXiv  Detail & Related papers  (2022-03-09T14:56:48Z)
• Enhanced Invertible Encoding for Learned Image Compression [40.21904131503064]
  In this paper, we propose an enhanced Invertible. Network with invertible neural networks (INNs) to largely mitigate the information loss problem for better compression. Experimental results on the Kodak, CLIC, and Tecnick datasets show that our method outperforms the existing learned image compression methods.
  arXiv  Detail & Related papers  (2021-08-08T17:32:10Z)
• 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)
• Modeling Lost Information in Lossy Image Compression [72.69327382643549]
  Lossy image compression is one of the most commonly used operators for digital images. We propose a novel invertible framework called Invertible Lossy Compression (ILC) to largely mitigate the information loss problem.
  arXiv  Detail & Related papers  (2020-06-22T04:04:56Z)
• 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)
• A Unified End-to-End Framework for Efficient Deep Image Compression [35.156677716140635]
  We propose a unified framework called Efficient Deep Image Compression (EDIC) based on three new technologies. Specifically, we design an auto-encoder style network for learning based image compression. Our EDIC method can also be readily incorporated with the Deep Video Compression (DVC) framework to further improve the video compression performance.
  arXiv  Detail & Related papers  (2020-02-09T14:21:08Z)

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.