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.
This paper reports an empirical investigation into increasing the efficiency of subjective data collection by reducing the length of test sequences below the recommended 10 seconds. Twenty-four observers viewed four 10 second reference sequences, in addition to four truncated versions of each: 7 seconds; 5 seconds; 3 seconds and 1.5 seconds. Results indicated that, compared to the 10 second sequences, the ability of observers to identify compression artefacts was significantly reduced only when viewing the 1.5 second sequences. These results indicate that, when using the DSCQS methodology, a significant benefit in the efficiency of subjective data collection can be gained by reducing the length of test sequences to potentially as low as 3 seconds, without a significant impact upon reliability.
This paper presents a parametric video compression framework which exploits both texture warping and dynamic
texture synthesis. A perspective motion model is employed to warp static textures and a dynamic texture model
is used to synthesise time-varying textures. An artefact-based video quality metric (AVM) is proposed which
prevents spatial and temporal artefacts and assesses the reconstructed video quality. This is validated using
both the VQEG database and subjective assessment, and shows competitive performance on both non-synthetic
and synthetic video content. Moreover, a local Rate-Quality Optimisation (RQO) strategy is developed based
on AVM in order to make a decision between waveform coding and texture warping/synthesis. The proposed
method has been integrated into an H.264 video coding framework with results offering significant bitrate savings
for similar visual quality (based on both AVM and subjective scores).