Super-Resolving Compressed Video in Coding Chain

• URL: http://arxiv.org/abs/2103.14247v1
• Date: Fri, 26 Mar 2021 03:39:54 GMT
• Title: Super-Resolving Compressed Video in Coding Chain
• Authors: Dewang Hou, Yang Zhao, Yuyao Ye, Jiayu Yang, Jian Zhang, Ronggang Wang
• Abstract summary: We present a mixed-resolution coding framework, which cooperates with a reference-based DCNN. In this novel coding chain, the reference-based DCNN learns the direct mapping from low-resolution (LR) compressed video to their high-resolution (HR) clean version at the decoder side.
• Score: 27.994055823226848
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scaling and lossy coding are widely used in video transmission and storage. Previous methods for enhancing the resolution of such videos often ignore the inherent interference between resolution loss and compression artifacts, which compromises perceptual video quality. To address this problem, we present a mixed-resolution coding framework, which cooperates with a reference-based DCNN. In this novel coding chain, the reference-based DCNN learns the direct mapping from low-resolution (LR) compressed video to their high-resolution (HR) clean version at the decoder side. We further improve reconstruction quality by devising an efficient deformable alignment module with receptive field block to handle various motion distances and introducing a disentangled loss that helps networks distinguish the artifact patterns from texture. Extensive experiments demonstrate the effectiveness of proposed innovations by comparing with state-of-the-art single image, video and reference-based restoration methods.

Related papers

• Implicit Neural Representation for Videos Based on Residual Connection [0.0]
  We propose a method that uses low-resolution frames as residual connection that is considered effective for image reconstruction. Experimental results show that our method outperforms the existing method, HNeRV, in PSNR for 46 of the 49 videos.
  arXiv  Detail & Related papers  (2024-06-15T10:10:48Z)
• Compression-Realized Deep Structural Network for Video Quality Enhancement [78.13020206633524]
  This paper focuses on the task of quality enhancement for compressed videos. Most of the existing methods lack a structured design to optimally leverage the priors within compression codecs. A new paradigm is urgently needed for a more conscious'' process of quality enhancement.
  arXiv  Detail & Related papers  (2024-05-10T09:18:17Z)
• VCISR: Blind Single Image Super-Resolution with Video Compression Synthetic Data [18.877077302923713]
  We present a video compression-based degradation model to synthesize low-resolution image data in the blind SISR task. Our proposed image synthesizing method is widely applicable to existing image datasets. By introducing video coding artifacts to SISR degradation models, neural networks can super-resolve images with the ability to restore video compression degradations.
  arXiv  Detail & Related papers  (2023-11-02T05:24:19Z)
• 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)
• Video Compression with Arbitrary Rescaling Network [8.489428003916622]
  We propose a rate-guided arbitrary rescaling network (RARN) for video resizing before encoding. The lightweight RARN structure can process FHD (1080p) content at real-time speed (91 FPS) and obtain a considerable rate reduction.
  arXiv  Detail & Related papers  (2023-06-07T07:15:18Z)
• Learned Video Compression via Heterogeneous Deformable Compensation Network [78.72508633457392]
  We propose a learned video compression framework via heterogeneous deformable compensation strategy (HDCVC) to tackle the problems of unstable compression performance. More specifically, the proposed algorithm extracts features from the two adjacent frames to estimate content-Neighborhood heterogeneous deformable (HetDeform) kernel offsets. Experimental results indicate that HDCVC achieves superior performance than the recent state-of-the-art learned video compression approaches.
  arXiv  Detail & Related papers  (2022-07-11T02:31:31Z)
• Reducing Redundancy in the Bottleneck Representation of the Autoencoders [98.78384185493624]
  Autoencoders are a type of unsupervised neural networks, which can be used to solve various tasks. We propose a scheme to explicitly penalize feature redundancies in the bottleneck representation. We tested our approach across different tasks: dimensionality reduction using three different dataset, image compression using the MNIST dataset, and image denoising using fashion MNIST.
  arXiv  Detail & Related papers  (2022-02-09T18:48:02Z)
• Perceptually-inspired super-resolution of compressed videos [18.72040343193715]
  spatial resolution adaptation is a technique which has often been employed in video compression to enhance coding efficiency. Recent work has employed advanced super-resolution methods based on convolutional neural networks (CNNs) to further improve reconstruction quality. In this paper, a perceptually-inspired super-resolution approach (M-SRGAN) is proposed for spatial upsampling of compressed video using a modified CNN model.
  arXiv  Detail & Related papers  (2021-06-15T13:50:24Z)
• 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)
• 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)

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.