BVI-Artefact: An Artefact Detection Benchmark Dataset for Streamed Videos

• URL: http://arxiv.org/abs/2312.08859v2
• Date: Thu, 7 Mar 2024 12:06:58 GMT
• Title: BVI-Artefact: An Artefact Detection Benchmark Dataset for Streamed Videos
• Authors: Chen Feng, Duolikun Danier, Fan Zhang, Alex Mackin, Andy Collins and David Bull
• Abstract summary: This work addresses the lack of a comprehensive benchmark for artefact detection within streamed PGC. Considering the ten most relevant artefact types encountered in video streaming, we collected and generated 480 video sequences. Results show the challenging nature of this tasks and indicate the requirement of more reliable artefact detection methods.
• Score: 7.5806062386946245
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Professionally generated content (PGC) streamed online can contain visual artefacts that degrade the quality of user experience. These artefacts arise from different stages of the streaming pipeline, including acquisition, post-production, compression, and transmission. To better guide streaming experience enhancement, it is important to detect specific artefacts at the user end in the absence of a pristine reference. In this work, we address the lack of a comprehensive benchmark for artefact detection within streamed PGC, via the creation and validation of a large database, BVI-Artefact. Considering the ten most relevant artefact types encountered in video streaming, we collected and generated 480 video sequences, each containing various artefacts with associated binary artefact labels. Based on this new database, existing artefact detection methods are benchmarked, with results showing the challenging nature of this tasks and indicating the requirement of more reliable artefact detection methods. To facilitate further research in this area, we have made BVI-Artifact publicly available at https://chenfeng-bristol.github.io/BVI-Artefact/

Related papers

• MVAD: A Multiple Visual Artifact Detector for Video Streaming [7.782835693566871]
  Visual artifacts are often introduced into streamed video content, due to prevailing conditions during content production and delivery. Existing detection methods often focus on a single type of artifact and determine the presence of an artifact through thresholding objective quality indices. We propose a Multiple Visual Artifact Detector, MVAD, for video streaming which, for the first time, is able to detect multiple artifacts using a single framework.
  arXiv  Detail & Related papers  (2024-05-31T21:56:04Z)
• Data-Independent Operator: A Training-Free Artifact Representation Extractor for Generalizable Deepfake Detection [105.9932053078449]
  In this work, we show that, on the contrary, the small and training-free filter is sufficient to capture more general artifact representations. Due to its unbias towards both the training and test sources, we define it as Data-Independent Operator (DIO) to achieve appealing improvements on unseen sources. Our detector achieves a remarkable improvement of $13.3%$, establishing a new state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-03-11T15:22:28Z)
• Reference-based Restoration of Digitized Analog Videotapes [28.773037051085318]
  We present a reference-based approach for the resToration of digitized Analog videotaPEs (TAPE) We leverage CLIP for zero-shot artifact detection to identify the cleanest frames of each video through textual prompts describing different artifacts. To address the absence of ground truth in real-world videos, we create a synthetic dataset of videos exhibiting artifacts that closely resemble those commonly found in analog videotapes.
  arXiv  Detail & Related papers  (2023-10-20T17:33:57Z)
• AVTENet: Audio-Visual Transformer-based Ensemble Network Exploiting Multiple Experts for Video Deepfake Detection [53.448283629898214]
  The recent proliferation of hyper-realistic deepfake videos has drawn attention to the threat of audio and visual forgeries. Most previous work on detecting AI-generated fake videos only utilize visual modality or audio modality. We propose an Audio-Visual Transformer-based Ensemble Network (AVTENet) framework that considers both acoustic manipulation and visual manipulation.
  arXiv  Detail & Related papers  (2023-10-19T19:01:26Z)
• Perceptual Artifacts Localization for Image Synthesis Tasks [59.638307505334076]
  We introduce a novel dataset comprising 10,168 generated images, each annotated with per-pixel perceptual artifact labels. A segmentation model, trained on our proposed dataset, effectively localizes artifacts across a range of tasks. We propose an innovative zoom-in inpainting pipeline that seamlessly rectifies perceptual artifacts in the generated images.
  arXiv  Detail & Related papers  (2023-10-09T10:22:08Z)
• Saliency-Aware Spatio-Temporal Artifact Detection for Compressed Video Quality Assessment [16.49357671290058]
  Compressed videos often exhibit visually annoying artifacts, known as Perceivable Temporal Artifacts (PEAs) In this paper, we investigate the influence of four spatial PEAs (i.e. blurring, blocking, bleeding, and ringing) and two temporal PEAs (i.e. flickering and floating) on video quality. Based on the six types of PEAs, a quality metric called Saliency-Aware Spatio-Temporal Artifacts Measurement (SSTAM) is proposed.
  arXiv  Detail & Related papers  (2023-01-03T12:48:27Z)
• Towards A Robust Deepfake Detector:Common Artifact Deepfake Detection Model [14.308886041268973]
  We propose a novel deepfake detection method named Common Artifact Deepfake Detection Model. We find that the main obstacle to learning common artifact features is that models are easily misled by the identity representation feature. Our method effectively reduces the influence of Implicit Identity Leakage and outperforms the state-of-the-art by a large margin.
  arXiv  Detail & Related papers  (2022-10-26T04:02:29Z)
• Voice-Face Homogeneity Tells Deepfake [56.334968246631725]
  Existing detection approaches contribute to exploring the specific artifacts in deepfake videos. We propose to perform the deepfake detection from an unexplored voice-face matching view. Our model obtains significantly improved performance as compared to other state-of-the-art competitors.
  arXiv  Detail & Related papers  (2022-03-04T09:08:50Z)
• 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)
• BBAND Index: A No-Reference Banding Artifact Predictor [55.42929350861115]
  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)
  arXiv  Detail & Related papers  (2020-02-27T03:05:26Z)

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.