Paper Info
Title
A Hybrid Neural Network For Chroma Intra Prediction
Abstract
For chroma intra prediction, previous methods exemplified by the Linear Model method (LM) usually assume a linear correlation between the luma and chroma components in a coding block. This assumption is inaccurate for complex image content or large blocks, and restricts the prediction accuracy. In this paper, we propose a chroma intra prediction method by exploiting both spatial and cross-channel correlations using a hybrid neural network. Specifically, we utilize a convolutional neural network to extract features from the reconstructed luma samples of the current block, as well as utilize a fully connected network to extract features from the neighboring reconstructed luma and chroma samples. The extracted twofold features are then fused to predict the chroma samples-Cb and Cr simultaneously. The proposed chroma intra prediction method is integrated into HEVC. Preliminary results show that, compared with HEVC plus LM, the proposed method achieves on average 0.2%, 3.1% and 2.0% BD-rate reduction on Y, Cb and Cr components, respectively, under All-Intra configuration.
Year
Venue
Keywords
2018
2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
Chroma intra prediction, convolutional neural network, fully connected network, hybrid neural network
Field
DocType
ISSN
Kernel (linear algebra),Iterative reconstruction,Pattern recognition,Convolutional neural network,Computer science,Feature extraction,Hybrid neural network,Artificial intelligence,Artificial neural network,Luma,Encoding (memory)
Conference
1522-4880
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Yue Li113342.96
Li Li26821.99
Zhu Li3259.14
jianchao yang47508282.48
xu ning52515.72
Dong Liu672174.92
Houqiang Li72090172.30