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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.
|Number of pages||6|
|Journal||IEEE Transactions on Circuits and Systems for Video Technology|
|Early online date||31 Oct 2018|
|Publication status||Published - 1 Jan 2019|
- Video compression
- spatial resolution adaptation
- temporal resolution adaptation
- perceptual video compression
- CNN-based super-resolution
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25 Jun 2019
Student thesis: Doctoral Thesis › Doctor of Philosophy (PhD)File