1. Video Compression Based on Spatio-Temporal Resolution Adaptation.
- Author
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Afonso, Mariana, Zhang, Fan, and Bull, David R.
- Subjects
- *
VIDEO compression , *VIDEO coding , *SPATIOTEMPORAL processes , *IMAGE quality analysis , *IMAGE compression , *ARTIFICIAL neural networks , *IMAGE processing , *SIGNAL convolution - Abstract
A video compression framework based on spatio-temporal resolution adaptation (ViSTRA) is proposed, which dynamically resamples the input video spatially and temporally during encoding, based on a quantisation-resolution decision, and reconstructs the full resolution video at the decoder. Temporal upsampling is performed using frame repetition, whereas a convolutional neural network super-resolution model is employed for spatial resolution upsampling. ViSTRA has been integrated into the high efficiency video coding reference software (HM 16.14). Experimental results verified via an international challenge show significant improvements, with BD-rate gains of 15% based on PSNR and an average MOS difference of 0.5 based on subjective visual quality tests. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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