Deep Learning-Based Luma and Chroma Fractional Interpolation in Video Coding

Motion compensated prediction is one of the essential methods to reduce temporal redundancy in inter coding. The target of motion compensated prediction is to predict the current frame from the list of reference frames. Recent video coding standards commonly use interpolation filters to obtain sub-p...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.112535-112543
Main Authors: Pham, Chi Do-Kim, Zhou, Jinjia
Format: Article
Language:English
Subjects:
Artificial neural networks
Coding standards
Convolution neural network (CNN)
Deep learning
Encoding
fractional interpolation
Image resolution
Interpolation
motion compensated prediction
Redundancy
Signal resolution
Training
Video coding
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Summary:Motion compensated prediction is one of the essential methods to reduce temporal redundancy in inter coding. The target of motion compensated prediction is to predict the current frame from the list of reference frames. Recent video coding standards commonly use interpolation filters to obtain sub-pixel for the best matching block located in the fractional position of the reference frame. However, the fixed filters are not flexible to adapt to the variety of natural video contents. Inspired by the success of Convolutional Neural Network (CNN) in super-resolution, we propose CNN-based fractional interpolation for Luminance (Luma) and Chrominance (Chroma) components in motion compensated prediction to improve the coding efficiency. Moreover, two syntax elements indicate interpolation methods for the Luminance and Chrominance components, have been added to bin-string and encoded by CABAC using regular mode. As a result, our proposal gains 2.9%, 0.3%, 0.6% Y, U, V BD-rate reduction, respectively, under low delay P configuration.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2935378