Low-complexity Deep Video Compression with A Distributed Coding Architecture

• URL: http://arxiv.org/abs/2303.11599v2
• Date: Sun, 2 Apr 2023 05:54:24 GMT
• Title: Low-complexity Deep Video Compression with A Distributed Coding Architecture
• Authors: Xinjie Zhang, Jiawei Shao, and Jun Zhang
• Abstract summary: Prevalent predictive coding-based video compression methods rely on a heavy encoder to reduce temporal redundancy. Traditional distributed coding methods suffer from a substantial performance gap to predictive coding ones. We propose the first end-to-end distributed deep video compression framework to improve rate-distortion performance.
• Score: 4.5885672744218
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prevalent predictive coding-based video compression methods rely on a heavy encoder to reduce temporal redundancy, which makes it challenging to deploy them on resource-constrained devices. Since the 1970s, distributed source coding theory has indicated that independent encoding and joint decoding with side information (SI) can achieve high-efficient compression of correlated sources. This has inspired a distributed coding architecture aiming at reducing the encoding complexity. However, traditional distributed coding methods suffer from a substantial performance gap to predictive coding ones. Inspired by the great success of learning-based compression, we propose the first end-to-end distributed deep video compression framework to improve the rate-distortion performance. A key ingredient is an effective SI generation module at the decoder, which helps to effectively exploit inter-frame correlations without computation-intensive encoder-side motion estimation and compensation. Experiments show that our method significantly outperforms conventional distributed video coding and H.264. Meanwhile, it enjoys 6-7x encoding speedup against DVC [1] with comparable compression performance. Code is released at https://github.com/Xinjie-Q/Distributed-DVC.

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)
• Computationally-Efficient Neural Image Compression with Shallow Decoders [43.115831685920114]
  This paper takes a step forward towards closing the gap in decoding complexity by using a shallow or even linear decoding transform resembling that of JPEG. We exploit the often asymmetrical budget between encoding and decoding, by adopting more powerful encoder networks and iterative encoding.
  arXiv  Detail & Related papers  (2023-04-13T03:38:56Z)
• Deep Lossy Plus Residual Coding for Lossless and Near-lossless Image Compression [85.93207826513192]
  We propose a unified and powerful deep lossy plus residual (DLPR) coding framework for both lossless and near-lossless image compression. We solve the joint lossy and residual compression problem in the approach of VAEs. In the near-lossless mode, we quantize the original residuals to satisfy a given $ell_infty$ error bound.
  arXiv  Detail & Related papers  (2022-09-11T12:11:56Z)
• Leveraging Bitstream Metadata for Fast, Accurate, Generalized Compressed Video Quality Enhancement [74.1052624663082]
  We develop a deep learning architecture capable of restoring detail to compressed videos. We show that this improves restoration accuracy compared to prior compression correction methods. We condition our model on quantization data which is readily available in the bitstream.
  arXiv  Detail & Related papers  (2022-01-31T18:56:04Z)
• Neural JPEG: End-to-End Image Compression Leveraging a Standard JPEG Encoder-Decoder [73.48927855855219]
  We propose a system that learns to improve the encoding performance by enhancing its internal neural representations on both the encoder and decoder ends. Experiments demonstrate that our approach successfully improves the rate-distortion performance over JPEG across various quality metrics.
  arXiv  Detail & Related papers  (2022-01-27T20:20:03Z)
• 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)
• Variable Rate Video Compression using a Hybrid Recurrent Convolutional Learning Framework [1.9290392443571382]
  This paper presents PredEncoder, a hybrid video compression framework based on the concept of predictive auto-encoding. A variable-rate block encoding scheme has been proposed in the paper that leads to remarkably high quality to bit-rate ratios.
  arXiv  Detail & Related papers  (2020-04-08T20:49:25Z)
• 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.