Spatial resolution adaptation framework for video compression

Mariana Afonso; Fan Zhang; David R. Bull

doi:10.1117/12.2320520

17 September 2018 Spatial resolution adaptation framework for video compression

Mariana Afonso, Fan Zhang, David R. Bull

Proceedings Volume 10752, Applications of Digital Image Processing XLI; 107520L (2018) https://doi.org/10.1117/12.2320520
Event: SPIE Optical Engineering + Applications, 2018, San Diego, California, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Mariana Afonso, Fan Zhang, and David R. Bull "Spatial resolution adaptation framework for video compression", Proc. SPIE 10752, Applications of Digital Image Processing XLI, 107520L (17 September 2018); https://doi.org/10.1117/12.2320520

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