Content Adaptive and Error Propagation Aware Deep Video Compression

• URL: http://arxiv.org/abs/2003.11282v1
• Date: Wed, 25 Mar 2020 09:04:24 GMT
• Title: Content Adaptive and Error Propagation Aware Deep Video Compression
• Authors: Guo Lu, Chunlei Cai, Xiaoyun Zhang, Li Chen, Wanli Ouyang, Dong Xu, Zhiyong Gao
• Abstract summary: 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.
• Score: 110.31693187153084
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, learning based video compression methods attract increasing attention. However, the previous works suffer from error propagation due to the accumulation of reconstructed error in inter predictive coding. Meanwhile, the previous learning based video codecs are also not adaptive to different video contents. To address these two problems, we propose a content adaptive and error propagation aware video compression system. Specifically, our method employs a joint training strategy by considering the compression performance of multiple consecutive frames instead of a single frame. Based on the learned long-term temporal information, our approach effectively alleviates error propagation in reconstructed frames. More importantly, instead of using the hand-crafted coding modes in the traditional compression systems, we design an online encoder updating scheme in our system. The proposed approach updates the parameters for encoder according to the rate-distortion criterion but keeps the decoder unchanged in the inference stage. Therefore, the encoder is adaptive to different video contents and achieves better compression performance by reducing the domain gap between the training and testing datasets. Our method is simple yet effective and outperforms the state-of-the-art learning based video codecs on benchmark datasets without increasing the model size or decreasing the decoding speed.

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)
• Prediction and Reference Quality Adaptation for Learned Video Compression [54.58691829087094]
  We propose a confidence-based prediction quality adaptation (PQA) module to provide explicit discrimination for the spatial and channel-wise prediction quality difference. We also propose a reference quality adaptation (RQA) module and an associated repeat-long training strategy to provide dynamic spatially variant filters for diverse reference qualities.
  arXiv  Detail & Related papers  (2024-06-20T09:03:26Z)
• Low-complexity Deep Video Compression with A Distributed Coding Architecture [4.5885672744218]
  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.
  arXiv  Detail & Related papers  (2023-03-21T05:34:04Z)
• Exploring Long- and Short-Range Temporal Information for Learned Video Compression [54.91301930491466]
  We focus on exploiting the unique characteristics of video content and exploring temporal information to enhance compression performance. For long-range temporal information exploitation, we propose temporal prior that can update continuously within the group of pictures (GOP) during inference. In that case temporal prior contains valuable temporal information of all decoded images within the current GOP. In detail, we design a hierarchical structure to achieve multi-scale compensation.
  arXiv  Detail & Related papers  (2022-08-07T15:57:18Z)
• 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)
• 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)
• 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)
• 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)
• 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.