A Video Post-Filter Deblocking Method Based on Temporal Boosting Residual Networks

Wang, Yunlong; Liu, Shaohui; Jiang, Feng; Sun, Xiaoshuai; Liu, Yongliang

doi:10.1109/icme.2019.00205

2019 IEEE International Conference on Multimedia and Expo (ICME) 2019

DOI: 10.1109/icme.2019.00205

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A Video Post-Filter Deblocking Method Based on Temporal Boosting Residual Networks

Yunlong Wang

Shaohui Liu

Feng Jiang

et al.

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2020

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PEA265: Perceptual Assessment of Video Compression Artifacts

Lin

Zhou

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

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The most widely used video encoders share a common hybrid coding framework that includes block-based motion estimation/compensation and block-based transform coding. Despite their high coding efficiency, the encoded videos often exhibit visually annoying artifacts, denoted as Perceivable Encoding Artifacts (PEAs), which significantly degrade the visual Qualityof-Experience (QoE) of end users. To monitor and improve visual QoE, it is crucial to develop subjective and objective measures that can identify and quantify various types of PEAs. In this work, we make the first attempt to build a large-scale subjectlabelled database composed of H.265/HEVC compressed videos containing various PEAs. The database, namely the PEA265 database, includes 4 types of spatial PEAs (i.e. blurring, blocking, ringing and color bleeding) and 2 types of temporal PEAs (i.e. flickering and floating). Each containing at least 60,000 image or video patches with positive and negative labels. To objectively identify these PEAs, we train Convolutional Neural Networks (CNNs) using the PEA265 database. It appears that state-of-theart ResNeXt is capable of identifying each type of PEAs with high accuracy. Furthermore, we define PEA pattern and PEA intensity measures to quantify PEA levels of compressed video sequence. We believe that the PEA265 database and our findings will benefit the future development of video quality assessment methods and perceptually motivated video encoders.

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PEA265: Perceptual Assessment of Video Compression Artifacts

Lin

Zhou

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

View full text Add to dashboard Cite

show abstract

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A Video Post-Filter Deblocking Method Based on Temporal Boosting Residual Networks

Cited by 1 publication

References 22 publications

PEA265: Perceptual Assessment of Video Compression Artifacts

PEA265: Perceptual Assessment of Video Compression Artifacts

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