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Found 40 papers, 8 papers with code
MTKD: Multi-Teacher Knowledge Distillation for Image Super-Resolution
Knowledge distillation (KD) has emerged as a promising technique in deep learning, typically employed to enhance a compact student network through learning from their high-performance but more complex teacher variant.
A Spatio-temporal Aligned SUNet Model for Low-light Video Enhancement
Distortions caused by low-light conditions are not only visually unpleasant but also degrade the performance of computer vision tasks.
Feature Denoising For Low-Light Instance Segmentation Using Weighted Non-Local Blocks
Instance segmentation for low-light imagery remains largely unexplored due to the challenges imposed by such conditions, for example shot noise due to low photon count, color distortions and reduced contrast.
Rate-Quality or Energy-Quality Pareto Fronts for Adaptive Video Streaming?
Adaptive video streaming is a key enabler for optimising the delivery of offline encoded video content.
BVI-Lowlight: Fully Registered Benchmark Dataset for Low-Light Video Enhancement
Low-light videos often exhibit spatiotemporal incoherent noise, leading to poor visibility and compromised performance across various computer vision applications.
Immersive Video Compression using Implicit Neural Representations
In this paper we, for the first time, extend their application to immersive (multi-view) videos, by proposing MV-HiNeRV, a new INR-based immersive video codec.
Compressing Deep Image Super-resolution Models
Deep learning techniques have been applied in the context of image super-resolution (SR), achieving remarkable advances in terms of reconstruction performance.
Comparative Study of Hardware and Software Power Measurements in Video Compression
The environmental impact of video streaming services has been discussed as part of the strategies towards sustainable information and communication technologies.
Full-reference Video Quality Assessment for User Generated Content Transcoding
In this work, we observe that existing full-/no-reference quality metrics fail to accurately predict the perceptual quality difference between transcoded UGC content and the corresponding unpristine references.
RankDVQA-mini: Knowledge Distillation-Driven Deep Video Quality Assessment
Deep learning-based video quality assessment (deep VQA) has demonstrated significant potential in surpassing conventional metrics, with promising improvements in terms of correlation with human perception.
BVI-Artefact: An Artefact Detection Benchmark Dataset for Streamed Videos
Professionally generated content (PGC) streamed online can contain visual artefacts that degrade the quality of user experience.
Accelerating Learnt Video Codecs with Gradient Decay and Layer-wise Distillation
In recent years, end-to-end learnt video codecs have demonstrated their potential to compete with conventional coding algorithms in term of compression efficiency.
Wavelet-based Topological Loss for Low-Light Image Denoising
Despite extensive research conducted in the field of image denoising, many algorithms still heavily depend on supervised learning and their effectiveness primarily relies on the quality and diversity of training data.
UGC Quality Assessment: Exploring the Impact of Saliency in Deep Feature-Based Quality Assessment
Preliminary results indicate that high correlations are achieved by using only deep features while adding saliency is not always boosting the performance.
HiNeRV: Video Compression with Hierarchical Encoding-based Neural Representation
Learning-based video compression is currently a popular research topic, offering the potential to compete with conventional standard video codecs.
Ranked #1 on
Video Reconstruction
on UVG
LDMVFI: Video Frame Interpolation with Latent Diffusion Models
Existing works on video frame interpolation (VFI) mostly employ deep neural networks that are trained by minimizing the L1, L2, or deep feature space distance (e. g. VGG loss) between their outputs and ground-truth frames.
BVI-VFI: A Video Quality Database for Video Frame Interpolation
In order to narrow this research gap, we have developed a new video quality database named BVI-VFI, which contains 540 distorted sequences generated by applying five commonly used VFI algorithms to 36 diverse source videos with various spatial resolutions and frame rates.
A Topological Loss Function: Image Denoising on a Low-Light Dataset
The loss function is a combination of $\ell_1$ or $\ell_2$ losses with the new persistence-based topological loss.
Enhancing HDR Video Compression through CNN-based Effective Bit Depth Adaptation
In this work, we modify the MFRNet network architecture to enable multiple frame processing, and the new network, multi-frame MFRNet, has been integrated into the EBDA framework using two Versatile Video Coding (VVC) host codecs: VTM 16. 2 and the Fraunhofer Versatile Video Encoder (VVenC 1. 4. 0).
FloLPIPS: A Bespoke Video Quality Metric for Frame Interpoation
Video frame interpolation (VFI) serves as a useful tool for many video processing applications.
Enhancing VVC with Deep Learning based Multi-Frame Post-Processing
This paper describes a CNN-based multi-frame post-processing approach based on a perceptually-inspired Generative Adversarial Network architecture, CVEGAN.
Sparse InSAR Data 3D Inpainting for Ground Deformation Detection Along the Rail Corridor
Monitoring of ground movement close to the rail corridor, such as that associated with landslips caused by ground subsidence and/or uplift, is of great interest for the detection and prevention of possible railway faults.
A CNN-based Post-Processor for Perceptually-Optimized Immersive Media Compression
In recent years, resolution adaptation based on deep neural networks has enabled significant performance gains for conventional (2D) video codecs.
RankDVQA: Deep VQA based on Ranking-inspired Hybrid Training
In recent years, deep learning techniques have shown significant potential for improving video quality assessment (VQA), achieving higher correlation with subjective opinions compared to conventional approaches.
A Subjective Quality Study for Video Frame Interpolation
Video frame interpolation (VFI) is one of the fundamental research areas in video processing and there has been extensive research on novel and enhanced interpolation algorithms.
Enhancing Deformable Convolution based Video Frame Interpolation with Coarse-to-fine 3D CNN
This paper presents a new deformable convolution-based video frame interpolation (VFI) method, using a coarse to fine 3D CNN to enhance the multi-flow prediction.
ST-MFNet: A Spatio-Temporal Multi-Flow Network for Frame Interpolation
Video frame interpolation (VFI) is currently a very active research topic, with applications spanning computer vision, post production and video encoding.
Ranked #1 on
Video Frame Interpolation
on SNU-FILM (easy)
ViSTRA3: Video Coding with Deep Parameter Adaptation and Post Processing
This paper presents a deep learning-based video compression framework (ViSTRA3).
Analysis of Vision-based Abnormal Red Blood Cell Classification
This paper presents an automated process utilising the advantages of machine learning to increase capacity and standardisation of cell abnormality detection, and its performance is analysed.
A Subjective Study on Videos at Various Bit Depths
Bit depth adaptation, where the bit depth of a video sequence is reduced before transmission and up-sampled during display, can potentially reduce data rates with limited impact on perceptual quality.
Enhancing VMAF through New Feature Integration and Model Combination
VMAF is a machine learning based video quality assessment method, originally designed for streaming applications, which combines multiple quality metrics and video features through SVM regression.
Texture-aware Video Frame Interpolation
Our study shows that video texture has significant impact on the performance of frame interpolation models and it is beneficial to have separate models specifically adapted to these texture classes, instead of training a single model that tries to learn generic motion.
Contextual colorization and denoising for low-light ultra high resolution sequences
Experimental results show that our method outperforms existing approaches in terms of subjective quality and that it is robust to variations in brightness levels and noise.
A simulation environment for drone cinematography
Methods for environmental image capture, 3D reconstruction (photogrammetry) and the creation of foreground assets are presented along with a flexible and user-friendly simulation interface.
Artificial Intelligence in the Creative Industries: A Review
We therefore conclude that, in the context of creative industries, maximum benefit from AI will be derived where its focus is human centric -- where it is designed to augment, rather than replace, human creativity.
Deep Learning Framework for Detecting Ground Deformation in the Built Environment using Satellite InSAR data
The large volumes of Sentinel-1 data produced over Europe are being used to develop pan-national ground motion services.
Atmospheric turbulence removal using convolutional neural network
This paper describes a novel deep learning-based method for mitigating the effects of atmospheric distortion.
A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets
As only a small proportion of volcanoes are deforming and atmospheric noise is ubiquitous, the use of machine learning for detecting volcanic unrest is more challenging.
DefectNET: multi-class fault detection on highly-imbalanced datasets
As a data-driven method, the performance of deep convolutional neural networks (CNN) relies heavily on training data.
Atmospheric turbulence mitigation for sequences with moving objects using recursive image fusion
This paper describes a new method for mitigating the effects of atmospheric distortion on observed sequences that include large moving objects.
