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) › peer-review

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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
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 - 1 Jan 2019

Keywords

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

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Vision for the Future-Full
Bull, D. R.
1/02/15 → 31/01/20
Project: Research

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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Professor David R Bull
- Dave.Bullbristol.acuk
- School of Computer Science - Professor of Signal Processing
- Visual Information Laboratory
- Bristol Vision Institute
Person: Academic , Group lead

Cite this

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title = "Video Compression based on Spatio-Temporal Resolution Adaptation",

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.",

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AB - 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.

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KW - temporal resolution adaptation

KW - perceptual video compression

KW - CNN-based super-resolution

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Video Compression based on Spatio-Temporal Resolution Adaptation

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Projects

Vision for the Future-Full

Student theses

Intelligent Resampling Methods for Video Compression

Profiles

Professor David R Bull

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