Online Video Super-Resolution with Convolutional Kernel Bypass Graft

• URL: http://arxiv.org/abs/2208.02470v1
• Date: Thu, 4 Aug 2022 05:46:51 GMT
• Title: Online Video Super-Resolution with Convolutional Kernel Bypass Graft
• Authors: Jun Xiao, Xinyang Jiang, Ningxin Zheng, Huan Yang, Yifan Yang, Yuqing Yang, Dongsheng Li, Kin-Man Lam
• Abstract summary: We propose an extremely low-latency VSR algorithm based on a novel kernel knowledge transfer method, named convolutional kernel bypass graft (CKBG) Experiment results show that our proposed method can process online video sequences up to 110 FPS, with very low model complexity and competitive SR performance.
• Score: 42.32318235565591
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning-based models have achieved remarkable performance in video super-resolution (VSR) in recent years, but most of these models are less applicable to online video applications. These methods solely consider the distortion quality and ignore crucial requirements for online applications, e.g., low latency and low model complexity. In this paper, we focus on online video transmission, in which VSR algorithms are required to generate high-resolution video sequences frame by frame in real time. To address such challenges, we propose an extremely low-latency VSR algorithm based on a novel kernel knowledge transfer method, named convolutional kernel bypass graft (CKBG). First, we design a lightweight network structure that does not require future frames as inputs and saves extra time costs for caching these frames. Then, our proposed CKBG method enhances this lightweight base model by bypassing the original network with ``kernel grafts'', which are extra convolutional kernels containing the prior knowledge of external pretrained image SR models. In the testing phase, we further accelerate the grafted multi-branch network by converting it into a simple single-path structure. Experiment results show that our proposed method can process online video sequences up to 110 FPS, with very low model complexity and competitive SR performance.

Related papers

• NVRC: Neural Video Representation Compression [13.131842990481038]
  We propose a novel INR-based video compression framework, Neural Video Representation Compression (NVRC) NVRC, for the first time, is able to optimize an INR-based video in a fully end-to-end manner. Our experiments show that NVRC outperforms many conventional and learning-based benchmark entropy.
  arXiv  Detail & Related papers  (2024-09-11T16:57:12Z)
• Boosting Neural Representations for Videos with a Conditional Decoder [28.073607937396552]
  Implicit neural representations (INRs) have emerged as a promising approach for video storage and processing. This paper introduces a universal boosting framework for current implicit video representation approaches.
  arXiv  Detail & Related papers  (2024-02-28T08:32:19Z)
• A Codec Information Assisted Framework for Efficient Compressed Video Super-Resolution [15.690562510147766]
  Video Super-Resolution (VSR) using recurrent neural network architecture is a promising solution due to its efficient modeling of long-range temporal dependencies. We propose a Codec Information Assisted Framework (CIAF) to boost and accelerate recurrent VSR models for compressed videos.
  arXiv  Detail & Related papers  (2022-10-15T08:48:29Z)
• Multi-encoder Network for Parameter Reduction of a Kernel-based Interpolation Architecture [10.08097582267397]
  Convolutional neural networks (CNNs) have been at the forefront of the recent advances in this field. Many of these networks require a lot of parameters, with more parameters meaning a heavier burden. This paper presents a method for parameter reduction for a popular flow-less kernel-based network.
  arXiv  Detail & Related papers  (2022-05-13T16:02:55Z)
• Learning Trajectory-Aware Transformer for Video Super-Resolution [50.49396123016185]
  Video super-resolution aims to restore a sequence of high-resolution (HR) frames from their low-resolution (LR) counterparts. Existing approaches usually align and aggregate video frames from limited adjacent frames. We propose a novel Transformer for Video Super-Resolution (TTVSR)
  arXiv  Detail & Related papers  (2022-04-08T03:37:39Z)
• Fast Online Video Super-Resolution with Deformable Attention Pyramid [172.16491820970646]
  Video super-resolution (VSR) has many applications that pose strict causal, real-time, and latency constraints, including video streaming and TV. We propose a recurrent VSR architecture based on a deformable attention pyramid (DAP)
  arXiv  Detail & Related papers  (2022-02-03T17:49:04Z)
• Self-Supervised Adaptation for Video Super-Resolution [7.26562478548988]
  Single-image super-resolution (SISR) networks can adapt their network parameters to specific input images. We present a new learning algorithm that allows conventional video super-resolution (VSR) networks to adapt their parameters to test video frames.
  arXiv  Detail & Related papers  (2021-03-18T08:30:24Z)
• DynaVSR: Dynamic Adaptive Blind Video Super-Resolution [60.154204107453914]
  DynaVSR is a novel meta-learning-based framework for real-world video SR. We train a multi-frame downscaling module with various types of synthetic blur kernels, which is seamlessly combined with a video SR network for input-aware adaptation. Experimental results show that DynaVSR consistently improves the performance of the state-of-the-art video SR models by a large margin.
  arXiv  Detail & Related papers  (2020-11-09T15:07:32Z)
• Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution [95.26202278535543]
  A simple solution is to split it into two sub-tasks: video frame (VFI) and video super-resolution (VSR) temporalsynthesis and spatial super-resolution are intra-related in this task. We propose a one-stage space-time video super-resolution framework, which directly synthesizes an HR slow-motion video from an LFR, LR video.
  arXiv  Detail & Related papers  (2020-02-26T16:59:48Z)
• Video Face Super-Resolution with Motion-Adaptive Feedback Cell [90.73821618795512]
  Video super-resolution (VSR) methods have recently achieved a remarkable success due to the development of deep convolutional neural networks (CNN) In this paper, we propose a Motion-Adaptive Feedback Cell (MAFC), a simple but effective block, which can efficiently capture the motion compensation and feed it back to the network in an adaptive way.
  arXiv  Detail & Related papers  (2020-02-15T13:14:10Z)

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.