Compressed-Domain-Aware Online Video Super-Resolution

• URL: http://arxiv.org/abs/2603.07694v1
• Date: Sun, 08 Mar 2026 15:52:48 GMT
• Title: Compressed-Domain-Aware Online Video Super-Resolution
• Abstract summary: In bandwidth-limited online video streaming, videos are usually downsampled and compressed.<n>Recent online video super-resolution (online VSR) approaches achieve promising results, but they are still compute-intensive.<n>We propose a compressed-domain-aware network (CDA-VSR) for online VSR to balance quality and efficiency.
• Score: 15.33081890735916
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In bandwidth-limited online video streaming, videos are usually downsampled and compressed. Although recent online video super-resolution (online VSR) approaches achieve promising results, they are still compute-intensive and fall short of real-time processing at higher resolutions, due to complex motion estimation for alignment and redundant processing of consecutive frames. To address these issues, we propose a compressed-domain-aware network (CDA-VSR) for online VSR, which utilizes compressed-domain information, including motion vectors, residual maps, and frame types to balance quality and efficiency. Specifically, we propose a motion-vector-guided deformable alignment module that uses motion vectors for coarse warping and learns only local residual offsets for fine-tuned adjustments, thereby maintaining accuracy while reducing computation. Then, we utilize a residual map gated fusion module to derive spatial weights from residual maps, suppressing mismatched regions and emphasizing reliable details. Further, we design a frame-type-aware reconstruction module for adaptive compute allocation across frame types, balancing accuracy and efficiency. On the REDS4 dataset, our CDA-VSR surpasses the state-of-the-art method TMP, with a maximum PSNR improvement of 0.13 dB while delivering more than double the inference speed. The code will be released at https://github.com/sspBIT/CDA-VSR.

Related papers

• D-Compress: Detail-Preserving LiDAR Range Image Compression for Real-Time Streaming on Resource-Constrained Robots [2.6111100616898852]
  D-Compress is a new detail-preserving and fast range image compression framework for real-time streaming.<n>It outperforms state-of-the-art (SOTA) compression methods in both geometric accuracy and downstream task performance.
  arXiv  Detail & Related papers  (2026-03-14T02:15:25Z)
• Trajectory-aware Shifted State Space Models for Online Video Super-Resolution [57.87099307245989]
  This paper presents a novel online VSR method based on Trajectory-aware Shifted SSMs (TS-Mamba)<n>TS-Mamba first constructs the trajectories within a video to select the most similar tokens from the previous frames.<n>Our TS-Mamba achieves state-of-the-art performance in most cases and over 22.7% reduction complexity (in MACs)
  arXiv  Detail & Related papers  (2025-08-14T08:42:15Z)
• QuantVSR: Low-Bit Post-Training Quantization for Real-World Video Super-Resolution [53.13952833016505]
  We propose a low-bit quantization model for real-world video super-resolution (VSR)<n>We use a calibration dataset to measure both spatial and temporal complexity for each layer.<n>We refine the FP and low-bit branches to achieve simultaneous optimization.
  arXiv  Detail & Related papers  (2025-08-06T14:35:59Z)
• FCA2: Frame Compression-Aware Autoencoder for Modular and Fast Compressed Video Super-Resolution [68.77813885751308]
  State-of-the-art (SOTA) compressed video super-resolution (CVSR) models face persistent challenges, including prolonged inference time, complex training pipelines, and reliance on auxiliary information.<n>We propose an efficient and scalable solution inspired by the structural and statistical similarities between hyperspectral images (HSI) and video data.<n>Our approach introduces a compression-driven dimensionality reduction strategy that reduces computational complexity, accelerates inference, and enhances the extraction of temporal information across frames.
  arXiv  Detail & Related papers  (2025-06-13T07:59:52Z)
• Collaborative Feedback Discriminative Propagation for Video Super-Resolution [66.61201445650323]
  Key success of video super-resolution (VSR) methods stems mainly from exploring spatial and temporal information. Inaccurate alignment usually leads to aligned features with significant artifacts. propagation modules only propagate the same timestep features forward or backward.
  arXiv  Detail & Related papers  (2024-04-06T22:08:20Z)
• TMP: Temporal Motion Propagation for Online Video Super-Resolution [20.56797955203068]
  Online video super-resolution (online-VSR) highly relies on an effective alignment module to aggregate temporal information. Most of the existing online-VSR methods estimate the motion fields of each frame separately to perform alignment. We propose an efficient Temporal Motion propagation (TMP) method, which leverages the continuity of motion field to achieve fast pixel-level alignment.
  arXiv  Detail & Related papers  (2023-12-15T16:17:34Z)
• Spatial-Temporal Transformer based Video Compression Framework [44.723459144708286]
  We propose a novel Spatial-Temporal Transformer based Video Compression (STT-VC) framework. It contains a Relaxed Deformable Transformer (RDT) with Uformer based offsets estimation for motion estimation and compensation, a Multi-Granularity Prediction (MGP) module based on multi-reference frames for prediction refinement, and a Spatial Feature Distribution prior based Transformer (SFD-T) for efficient temporal-spatial joint residual compression. Experimental results demonstrate that our method achieves the best result with 13.5% BD-Rate saving over VTM.
  arXiv  Detail & Related papers  (2023-09-21T09:23:13Z)
• 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)
• Optical-Flow-Reuse-Based Bidirectional Recurrent Network for Space-Time Video Super-Resolution [52.899234731501075]
  Space-time video super-resolution (ST-VSR) simultaneously increases the spatial resolution and frame rate for a given video. Existing methods typically suffer from difficulties in how to efficiently leverage information from a large range of neighboring frames. We propose a coarse-to-fine bidirectional recurrent neural network instead of using ConvLSTM to leverage knowledge between adjacent frames.
  arXiv  Detail & Related papers  (2021-10-13T15:21:30Z)
• 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.