BBAND Index: A No-Reference Banding Artifact Predictor
- URL: http://arxiv.org/abs/2002.11891v1
- Date: Thu, 27 Feb 2020 03:05:26 GMT
- Title: BBAND Index: A No-Reference Banding Artifact Predictor
- Authors: Zhengzhong Tu, Jessie Lin, Yilin Wang, Balu Adsumilli, and Alan C.
Bovik
- Abstract summary: Banding artifact, or false contouring, is a common video compression impairment.
We propose a new distortion-specific no-reference video quality model for predicting banding artifacts, called the Blind BANding Detector (BBAND index)
- Score: 55.42929350861115
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Banding artifact, or false contouring, is a common video compression
impairment that tends to appear on large flat regions in encoded videos. These
staircase-shaped color bands can be very noticeable in high-definition videos.
Here we study this artifact, and propose a new distortion-specific no-reference
video quality model for predicting banding artifacts, called the Blind BANding
Detector (BBAND index). BBAND is inspired by human visual models. The proposed
detector can generate a pixel-wise banding visibility map and output a banding
severity score at both the frame and video levels. Experimental results show
that our proposed method outperforms state-of-the-art banding detection
algorithms and delivers better consistency with subjective evaluations.
Related papers
- GRACE: Graph-Regularized Attentive Convolutional Entanglement with Laplacian Smoothing for Robust DeepFake Video Detection [7.591187423217017]
This paper introduces a novel method for robust DeepFake video detection based on graph convolutional network with graph Laplacian.
The proposed method delivers state-of-the-art performance in DeepFake video detection under noisy face sequences.
arXiv Detail & Related papers (2024-06-28T14:17:16Z) - FS-BAND: A Frequency-Sensitive Banding Detector [55.59101150019851]
Banding artifact, as known as staircase-like contour, is a common quality annoyance that happens in compression, transmission, etc.
We propose a no-reference banding detection model to capture and evaluate banding artifacts, called the Frequency-Sensitive BANding Detector (FS-BAND)
Experimental results show that the proposed FS-BAND method outperforms state-of-the-art image quality assessment (IQA) approaches with higher accuracy in banding classification task.
arXiv Detail & Related papers (2023-11-30T03:20:42Z) - Explaining Deepfake Detection by Analysing Image Matching [13.251308261180805]
This paper aims to interpret how deepfake detection models learn artifact features of images when just supervised by binary labels.
Deepfake detection models implicitly learn artifact-relevant visual concepts through the FST-Matching.
We propose the FST-Matching Deepfake Detection Model to boost the performance of forgery detection on compressed videos.
arXiv Detail & Related papers (2022-07-20T06:23:11Z) - Robust Unsupervised Video Anomaly Detection by Multi-Path Frame
Prediction [61.17654438176999]
We propose a novel and robust unsupervised video anomaly detection method by frame prediction with proper design.
Our proposed method obtains the frame-level AUROC score of 88.3% on the CUHK Avenue dataset.
arXiv Detail & Related papers (2020-11-05T11:34:12Z) - Coherent Loss: A Generic Framework for Stable Video Segmentation [103.78087255807482]
We investigate how a jittering artifact degrades the visual quality of video segmentation results.
We propose a Coherent Loss with a generic framework to enhance the performance of a neural network against jittering artifacts.
arXiv Detail & Related papers (2020-10-25T10:48:28Z) - Adaptive Debanding Filter [55.42929350861115]
Banding artifacts manifest as staircase-like color bands on pictures or video frames.
We propose a content-adaptive smoothing filtering followed by dithered quantization, as a post-processing module.
Experimental results show that our proposed debanding filter outperforms state-of-the-art false contour removing algorithms both visually and quantitatively.
arXiv Detail & Related papers (2020-09-22T20:44:20Z) - A Self-Reasoning Framework for Anomaly Detection Using Video-Level
Labels [17.615297975503648]
Alous event detection in surveillance videos is a challenging and practical research problem among image and video processing community.
We propose a weakly supervised anomaly detection framework based on deep neural networks which is trained in a self-reasoning fashion using only video-level labels.
The proposed framework has been evaluated on publicly available real-world anomaly detection datasets including UCF-crime, ShanghaiTech and Ped2.
arXiv Detail & Related papers (2020-08-27T02:14:15Z) - VideoForensicsHQ: Detecting High-quality Manipulated Face Videos [77.60295082172098]
We show how the performance of forgery detectors depends on the presence of artefacts that the human eye can see.
We introduce a new benchmark dataset for face video forgery detection, of unprecedented quality.
arXiv Detail & Related papers (2020-05-20T21:17:43Z)
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.