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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 (CNN) superresolution model is employed for spatial resolution upsampling. ViSTRA has been integrated into the High Efficiency Video Coding (HEVC) 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.
Original language | English |
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Pages (from-to) | 275-280 |
Number of pages | 6 |
Journal | IEEE Transactions on Circuits and Systems for Video Technology |
Volume | 29 |
Issue number | 1 |
Early online date | 31 Oct 2018 |
DOIs | |
Publication status | Published - 1 Jan 2019 |
Keywords
- Video compression
- spatial resolution adaptation
- temporal resolution adaptation
- perceptual video compression
- CNN-based super-resolution
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Dive into the research topics of 'Video Compression based on Spatio-Temporal Resolution Adaptation'. Together they form a unique fingerprint.Projects
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Student theses
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Intelligent Resampling Methods for Video Compression
Author: Fernandez Afonso, M., 25 Jun 2019Supervisor: Agrafiotis, D. (Supervisor) & Bull, D. (Supervisor)
Student thesis: Doctoral Thesis › Doctor of Philosophy (PhD)
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Profiles
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Professor David R Bull
- School of Computer Science - Professor of Signal Processing
- Visual Information Laboratory
- Bristol Vision Institute
Person: Academic , Group lead