Video intra prediction using convolutional encoder decoder network

Intra prediction is an effective method for video coding to remove the spatial redundancy of content. Classical intra prediction method usually creates a prediction block by extrapolating the encoded pixels surrounding the target block. However, existing methods cannot guarantee the prediction effic...

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Vydané v:Neurocomputing (Amsterdam) Ročník 394; s. 168 - 177
Hlavní autori: Jin, Zhipeng, An, Ping, Shen, Liquan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 21.06.2020
Predmet:
Convolutional encoder-decoder network (CED)
High Efficiency Video Coding (HEVC)
Image inpainting
Intra prediction
Video coding
Video coding
Intra prediction
Image inpainting
Convolutional encoder-decoder network (CED)
High Efficiency Video Coding (HEVC)
ISSN:0925-2312, 1872-8286
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Popis
Shrnutí:Intra prediction is an effective method for video coding to remove the spatial redundancy of content. Classical intra prediction method usually creates a prediction block by extrapolating the encoded pixels surrounding the target block. However, existing methods cannot guarantee the prediction efficiency for rich textural structure, especially when weak spatial correlation exists between the target block and reference pixels. To remedy this issue, this paper proposes a novel intra prediction method via convolutional encoder-decoder network, which we term IPCED. IPCED can learn and extract the internal representation of reference blocks, and progressively generate a prediction block from this representation. IPCED is a data-driven method, which represents an improvement over hand-crafted methods, and is capable of improving the accuracy of intra prediction. Extensive experimental results demonstrate that IPCED can generate higher-quality intra prediction results, achieves 3.41%, 3.07% and 3.44% bitrate saving for the Y/Cb/Cr channel compared with HEVC baseline, which is significantly beyond existing methods.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2019.02.064