This paper presents a resolution adaptation framework for video compression. It dynamically applies spatial resampling, trading off the relationship between spatial resolution and quantization. A learning-based Quantization-Resolution Optimization (QRO) module, trained on a large database of video content, determines the optimal spatial resolution among multiple options, based on spatial and temporal video features of the uncompressed video frames. In order to improve the quality of upscaled videos, a modified CNN-based single image super-resolution method is employed at the decoder. This super-resolution model has been trained using compressed content from the same training database. The proposed resolution adaptation framework was integrated with the High Efficiency Video Coding (HEVC) reference software, HM 16.18, and tested on UHD content from several databases including videos from the JVET (Joint Video Exploration Team) test set. Experimental results show that the proposed method offers significant overall bit rate savings for a wide range of bitrates compared with the original HEVC HM 16.18, with average BD-rate savings of 12% (based on PSNR) and 15% (based on VMAF) and lower encoding complexity.

Date Published: 17 September 2018
PDF: 10 pages
Proc. SPIE 10752, Applications of Digital Image Processing XLI, 107520L (17 September 2018); doi: 10.1117/12.2320520

Published in SPIE Proceedings Vol. 10752:
Applications of Digital Image Processing XLI
Andrew G. Tescher, Editor(s)