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Found 23 papers, 11 papers with code
Ti-Patch: Tiled Physical Adversarial Patch for no-reference video quality metrics
This paper proposes a new method for testing quality metrics vulnerability in the physical space.
Adversarial purification for no-reference image-quality metrics: applicability study and new methods
In this study, we aim to cover that case and check the transferability of adversarial purification defences from image classifiers to IQA methods.
IOI: Invisible One-Iteration Adversarial Attack on No-Reference Image- and Video-Quality Metrics
No-reference image- and video-quality metrics are widely used in video processing benchmarks.
Comparing the Robustness of Modern No-Reference Image- and Video-Quality Metrics to Adversarial Attacks
Nowadays, neural-network-based image- and video-quality metrics perform better than traditional methods.
BASED: Benchmarking, Analysis, and Structural Estimation of Deblurring
This paper discusses the challenges of evaluating deblurring-methods quality and proposes a reduced-reference metric based on machine learning.
Fast Adversarial CNN-based Perturbation Attack on No-Reference Image- and Video-Quality Metrics
The proposed attack (FACPA) can be exploited as a preprocessing step in real-time video processing and compression algorithms.
Compressed Video Quality Assessment for Super-Resolution: a Benchmark and a Quality Metric
We assessed 17 state-ofthe-art SR models using our benchmark and evaluated their ability to preserve scene context and their susceptibility to compression artifacts.
Color Mismatches in Stereoscopic Video: Real-World Dataset and Deep Correction Method
We propose a real-world dataset of stereoscopic videos for color-mismatch correction.
Applicability limitations of differentiable full-reference image-quality
We propose a series of neural-network preprocessing models that increase DISTS by up to 34. 5%, LPIPS by up to 36. 8%, VIF by up to 98. 0%, and HaarPSI by up to 22. 6% in the case of JPEG-compressed images.
Video compression dataset and benchmark of learning-based video-quality metrics
Video-quality measurement is a critical task in video processing.
Bit-depth enhancement detection for compressed video
This problem is complex; it involves detecting outcomes of different dequantization algorithms in the presence of compression that strongly affects the least-significant bits (LSBs) in the video frames.
Universal Perturbation Attack on Differentiable No-Reference Image- and Video-Quality Metrics
Indeed, if an attack can confuse the metric, an attacker can easily increase quality scores.
Combining Contrastive and Supervised Learning for Video Super-Resolution Detection
To explain how the method detects videos, we systematically review the major components of our framework - in particular, we show that most data-augmentation approaches hinder the learning of the method.
Towards True Detail Restoration for Super-Resolution: A Benchmark and a Quality Metric
To analyze the detail-restoration capabilities of image and video SR models, we developed a benchmark based on our own video dataset, which contains complex patterns that SR models generally fail to correctly restore.
Shot boundary detection method based on a new extensive dataset and mixed features
Shot boundary detection in video is one of the key stages of video data processing.
Objective video quality metrics application to video codecs comparisons: choosing the best for subjective quality estimation
Quality assessment plays a key role in creating and comparing video compression algorithms.
Hacking VMAF and VMAF NEG: vulnerability to different preprocessing methods
We also show that some preprocessing methods can increase VMAF NEG scores by up to 23. 6%.
Deep Two-Stage High-Resolution Image Inpainting
In this article, we propose a method that solves the problem of inpainting arbitrary-size images.
Barriers towards no-reference metrics application to compressed video quality analysis: on the example of no-reference metric NIQE
This paper analyses the application of no-reference metric NIQE to the task of video-codec comparison.
Ranked #20 on
Video Quality Assessment
on MSU NR VQA Database
Predicting video saliency using crowdsourced mouse-tracking data
This paper presents a new way of getting high-quality saliency maps for video, using a cheaper alternative to eye-tracking data.
Improving Video Compression With Deep Visual-Attention Models
We propose a compression method that uses a saliency model to adaptively compress frame areas in accordance with their predicted saliency.
Shot-by-Shot Movie Version Comparison
Some movies are released in two or more versions.
ACCURATE METHOD OF TEMPORAL-SHIFT ESTIMATION FOR 3D VIDEO
Video synchronization is a fundamental computer-vision task that is necessary for a wide range of applications.
