Artifacts Detection and Error Block Analysis from Broadcasted Videos
Md Mehedi Hasan, Tasneem Rahman, Kiok Ahn, Oksam Chae

TL;DR
This paper presents a real-time, lightweight system for detecting and analyzing errors in broadcasted videos using edge gradient information and texture pattern classification, improving video quality assessment and error management.
Contribution
Develops a novel real-time video error detection method utilizing edge gradient and texture analysis, suitable for broadcast and surveillance applications.
Findings
Efficient error detection with low computation time.
Effective classification of block errors using texture patterns.
Successful validation on broadcasted videos and datasets.
Abstract
With the advancement of IPTV and HDTV technology, previous subtle errors in videos are now becoming more prominent because of the structure oriented and compression based artifacts. In this paper, we focus towards the development of a real-time video quality check system. Light weighted edge gradient magnitude information is incorporated to acquire the statistical information and the distorted frames are then estimated based on the characteristics of their surrounding frames. Then we apply the prominent texture patterns to classify them in different block errors and analyze them not only in video error detection application but also in error concealment, restoration and retrieval. Finally, evaluating the performance through experiments on prominent datasets and broadcasted videos show that the proposed algorithm is very much efficient to detect errors for video broadcast and…
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Taxonomy
TopicsImage and Video Quality Assessment · Advanced Image Processing Techniques · Image and Signal Denoising Methods
