Abstract
Macroblocking is a type of widely observed video artifact where severe block-shaped artifacts appear in video frames. Macroblocking may be produced by heavy lossy compression but is visually most annoying when transmission error such as packet loss occurs during network video transmission. Since receivers do not have access to the pristine-quality original videos, macroblocking detection needs to be performed using no-reference (NR) approaches. This paper presents our recent research progress on detecting macroblocking caused by packet loss. We build the first of its kind macroblocking database that contains approximately 150,000 video frames with labels. Using the database, We make initial attempts of using transfer learning based deep learning techniques to tackle this challenging problem with and without using the Apache Spark big data processing framework. Our results show that it is beneficiary to use Spark. We believe that the current work will help the future development of macroblocking detection methods.
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Rajasekar, G., Wang, Z. (2020). Detecting Macroblocking in Images Caused by Transmission Error. In: Campilho, A., Karray, F., Wang, Z. (eds) Image Analysis and Recognition. ICIAR 2020. Lecture Notes in Computer Science(), vol 12131. Springer, Cham. https://doi.org/10.1007/978-3-030-50347-5_7
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DOI: https://doi.org/10.1007/978-3-030-50347-5_7
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