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• W2981878426 abstract "Video compression artifact reduction aims to recover high-quality videos from low-quality compressed videos. Most existing approaches use a single neighboring frame or a pair of neighboring frames (preceding and/or following the target frame) for this task. Furthermore, as frames of high quality overall may contain low-quality patches, and high-quality patches may exist in frames of low quality overall, current methods focusing on nearby peak-quality frames (PQFs) may miss high-quality details in low-quality frames. To remedy these shortcomings, in this paper we propose a novel end-to-end deep neural network called non-local ConvLSTM (NL-ConvLSTM in short) that exploits multiple consecutive frames. An approximate non-local strategy is introduced in NL-ConvLSTM to capture global motion patterns and trace the spatiotemporal dependency in a video sequence. This approximate strategy makes the non-local module work in a fast and low space-cost way. Our method uses the preceding and following frames of the target frame to generate a residual, from which a higher quality frame is reconstructed. Experiments on two datasets show that NL-ConvLSTM outperforms the existing methods." @default.
• W2981878426 created "2019-11-01" @default.
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• W2981878426 date "2019-10-27" @default.
• W2981878426 modified "2023-09-23" @default.
• W2981878426 title "Non-Local ConvLSTM for Video Compression Artifact Reduction" @default.
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• W2981878426 doi "https://doi.org/10.48550/arxiv.1910.12286" @default.
• W2981878426 hasPublicationYear "2019" @default.
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