BVI-Artefact: Benchmarking Artefact Detection in Streamed Videos

Core Concepts

The author addresses the lack of a comprehensive benchmark for artefact detection within streamed PGC by creating and validating the BVI-Artefact database, highlighting the challenging nature of artefact detection tasks and the need for more reliable methods.

Abstract

The content introduces the BVI-Artefact database, focusing on detecting visual artefacts in streamed videos. It discusses the challenges of detecting specific artefacts without a pristine reference and presents a detailed methodology for creating the database. The study benchmarks existing artefact detection methods using the database, revealing the limitations and areas for improvement in current approaches. Overall, it emphasizes the importance of robust and accurate artefact detection methods for enhancing user experience in video streaming.

Stats

480 video sequences collected with various artefacts.
60 source sequences from public video databases.
10 common visual artefacts introduced in streamed PGC content.
120 compressed videos with different quantization levels.
Binary labels provided for each sequence indicating presence of artefacts.

Quotes

"No-reference manner prediction is limited in providing an underlying reason for quality degradation."
"Existing work mainly focuses on offering single-dimensional predictions for overall video quality."
"The results reveal the challenging nature of detecting specific artefacts without a pristine reference."

Key Insights Distilled From

BVI-Artefact

by Chen Feng,Du... at arxiv.org 03-08-2024

https://arxiv.org/pdf/2312.08859.pdf

Deeper Inquiries

How can multiple co-existing artefacts impact current detection methods?

When multiple artefacts co-exist in a video, it can significantly impact the performance of current detection methods. Most existing detection algorithms are designed to identify a single type of artefact at a time, assuming that the video contains only one specific distortion. However, in real-world scenarios like streamed PGC content, various artefacts can interact with each other simultaneously. This interaction complicates the detection process as different distortions may mask or amplify each other's effects, leading to misinterpretations by the detection algorithms.
The presence of multiple co-existing artefacts challenges the assumption of independence between different types of distortions made by many detection models. The complexity introduced by these interactions can result in decreased accuracy and reliability in identifying individual visual anomalies within videos. Therefore, current methods need to be enhanced to consider and account for these interplays among different types of artefacts when detecting visual impairments.

What improvements are needed to enhance accuracy in detecting visual artefacts?

To enhance accuracy in detecting visual artifacts, several improvements are necessary:

Multi-Artefact Detection Models: Develop advanced models capable of detecting and distinguishing between multiple co-existing artifacts within videos. These models should consider how different distortions interact and affect each other's visibility.

Data Augmentation Techniques: Utilize data augmentation techniques that simulate realistic scenarios where various artifacts occur simultaneously. By training models on diverse datasets containing complex combinations of distortions, they can better generalize to real-world situations.

Deep Learning Architectures: Explore more sophisticated deep learning architectures that have improved capabilities for feature extraction and representation learning from complex data patterns present in videos with multiple artifacts.

Fine-tuning Strategies: Implement fine-tuning strategies that adapt pre-trained models specifically for multi-artefact detection tasks while considering the nuances of interactions between different types of distortions.

Frame-Level Analysis: Incorporate frame-level analysis into detection algorithms for certain types of artifacts where temporal information is crucial (e.g., motion-related artifacts). This approach allows for a more detailed examination of artifact presence across frames.

6 .Robust Evaluation Metrics: Develop robust evaluation metrics that assess not only individual artifact detections but also their interactions within videos accurately.

How can real-world scenarios be better simulated to improve benchmarking databases?

Improving benchmarking databases involves simulating real-world scenarios effectively:
1 .Diverse Artefact Combinations: Include diverse combinations of common visual artifacts encountered during streaming processes such as acquisition errors, compression issues, transmission errors etc., ensuring that datasets reflect practical complexities seen in actual PGC content streams.
2 .Varied Content Types: Incorporate varied content genres (sports events, drama scenes) and environmental settings (indoor vs outdoor) into benchmarking databases to capture how different contexts influence artifact visibility.
3 .Temporal Dynamics: Consider temporal dynamics by including dynamic changes over time like scene transitions or fast-moving objects which could introduce additional challenges for artifact detectors.
4 .User Experience Factors: Integrate user experience factors such as perceptual quality degradation due to combined artifact effects into database design so that benchmarks align closely with human perception standards.
5 .Subjective Assessment Data: Supplement objective measures with subjective assessment data collected from human observers evaluating perceived quality under realistic conditions; this provides valuable insights into how well detectors align with human judgments.
6 .Continuous Updates: Regularly update benchmarking databases with new samples reflecting evolving streaming technologies and emerging artifact types observed in modern PGC streams; this ensures relevance and applicability over time
By incorporating these elements thoughtfully into benchmarking databases like BVI-Artefact , researchers can create more comprehensive test sets reflectiveof real-world challenges facedin streamed video content analysis research efforts