Video Compression based on Spatio-Temporal Resolution Adaptation

Mariana Afonso; Aaron Zhang; David Bull

doi:10.1109/TCSVT.2018.2878952

Video Compression based on Spatio-Temporal Resolution Adaptation

Mariana Afonso, Aaron Zhang, David Bull

Research output: Contribution to journal › Article (Academic Journal)

10 Citations (Scopus)

675 Downloads (Pure)

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
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	https://doi.org/10.1109/TCSVT.2018.2878952
Publication status	Published - Jan 2019

Keywords

Video compression
spatial resolution adaptation
temporal resolution adaptation
perceptual video compression
CNN-based super-resolution

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10.1109/TCSVT.2018.2878952

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Student Theses

Intelligent Resampling Methods for Video Compression

Author: Fernandez Afonso, M., 25 Jun 2019

Supervisor: Agrafiotis, D. (Supervisor) & Bull, D. (Supervisor)

Student thesis: Doctoral Thesis › Doctor of Philosophy (PhD)

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Cite this

Afonso, M., Zhang, A., & Bull, D. (2019). Video Compression based on Spatio-Temporal Resolution Adaptation. IEEE Transactions on Circuits and Systems for Video Technology, 29(1), 275-280. https://doi.org/10.1109/TCSVT.2018.2878952

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