Learning-Based Multi-Frame Video Quality Enhancement

Tong, Junchao; Wu, Xilin; Ding, Dandan; Zhu, Zheng; Liu, Zoe

doi:10.1109/icip.2019.8803786

Cited by 16 publications

(2 citation statements)

References 2 publications

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“…Then, Guan et al [192] upgraded MFQE-1.0 to MFQE-2.0 by replacing QE-net using a dense CNN model, leading to better performance and less complexity. Later on, Tong et al [193] suggested using FlowNet2 for temporal frame alignment (instead of default STMC), yielding 0.23-dB PSNR gain over the original MFQE-1.0. Similarly, FlowNet2 is also used in [194] for improved efficiency.…”

Section: B Postfilteringmentioning

confidence: 99%

Advances in Video Compression System Using Deep Neural Network: A Review and Case Studies

Ding

Chen

et al. 2021

Proc. IEEE

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Significant advances in video compression systems have been made in the past several decades to satisfy the near-exponential growth of Internet-scale video traffic. From the application perspective, we have identified three major functional blocks, including preprocessing, coding, and postprocessing, which have been continuously investigated to maximize the end-user quality of experience (QoE) under a limited bit rate budget. Recently, artificial intelligence (AI)-powered techniques have shown great potential to further increase the efficiency of the aforementioned functional blocks, both individually and jointly. In this article, we review recent technical advances in video compression systems extensively, with an emphasis on deep neural network (DNN)based approaches, and then present three comprehensive case studies. On preprocessing, we show a switchable texturebased video coding example that leverages DNN-based scene understanding to extract semantic areas for the improvement Manuscript

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Section: B Postfilteringmentioning

confidence: 99%

Advances in Video Compression System Using Deep Neural Network: A Review and Case Studies

Ding

Chen

et al. 2021

Proc. IEEE

Self Cite

View full text Add to dashboard Cite

show abstract

“…Then, Guan et al [193] upgraded MFQE-1.0 to MFQE-2.0 by replacing QE-net using a dense CNN model, leading to better performance and less complexity. Later on, Tong et al [194] suggested using FlowNet2 in MFQE-1.0 for temporal frame alignment (instead of default STMC), yielding 0.23 dB PSNR gain over the original MFQE-1.0. Similarly, FlowNet2 is also used in [195] for improved efficiency.…”

Section: B Post Filteringmentioning

confidence: 99%

Advances In Video Compression System Using Deep Neural Network: A Review And Case Studies

Ding¹,

Ma²,

Chen³

et al. 2021

Preprint

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Significant advances in video compression system have been made in the past several decades to satisfy the nearly exponential growth of Internet-scale video traffic. From the application perspective, we have identified three major functional blocks including pre-processing, coding, and postprocessing, that have been continuously investigated to maximize the end-user quality of experience (QoE) under a limited bit rate budget. Recently, artificial intelligence (AI) powered techniques have shown great potential to further increase the efficiency of the aforementioned functional blocks, both individually and jointly. In this article, we review extensively recent technical advances in video compression system, with an emphasis on deep neural network (DNN)-based approaches; and then present three comprehensive case studies. On pre-processing, we show a switchable texture-based video coding example that leverages DNN-based scene understanding to extract semantic areas for the improvement of subsequent video coder. On coding, we present an end-to-end neural video coding framework that takes advantage of the stacked DNNs to efficiently and compactly code input raw videos via fully data-driven learning. On post-processing, we demonstrate two neural adaptive filters to respectively facilitate the in-loop and post filtering for the enhancement of compressed frames. Finally, a companion website hosting the contents developed in this work can be accessed publicly at https://purdueviper.github.io/dnn-coding/.

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A video compression artifact reduction approach combined with quantization parameters estimation

Shuai

Qing²,

Zhang³

et al. 2022

J Supercomput

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Learning-Based Multi-Frame Video Quality Enhancement

Cited by 16 publications

References 2 publications

Advances in Video Compression System Using Deep Neural Network: A Review and Case Studies

Advances in Video Compression System Using Deep Neural Network: A Review and Case Studies

Advances In Video Compression System Using Deep Neural Network: A Review And Case Studies

A video compression artifact reduction approach combined with quantization parameters estimation

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