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17 September 2018 Spatial resolution adaptation framework for video compression
Mariana Afonso, Fan Zhang, David R. Bull
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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
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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Cited by 9 scholarly publications and 19 patents.
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KEYWORDS
Video

Video compression

Spatial resolution

Video coding

Video processing

Super resolution

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