Video compression is complicated by degradation in User Generated Content (UGC). Preprocessing the data before encoding improves compression. However the impact of the preprocessor depends not only on the codec and the filter strength of the preprocessor being used but also on the target bitrate of the encode and the level of degradation. In this paper we present a framework for modelling this relationship and estimating the optimal filter strength for a particular codec/preprocessor/bitrate/degradation combination. We examine two preprocessors based on classical and DNN ideas, and two codecs AV1, VP9. We find that up to 2dB of quality gain can result from preprocessing at constant bitrate and our estimator is accurate enough to capture most of these gains.
The technology climate for video streaming has vastly changed during 2020. Since the pandemic, video traffic over the internet has increased dramatically. This has clearly put increased interest in the bitrate/quality tradeoff for video compression for applications in video streaming and real time video communications. As far as we know, the impact of different artefacts on that tradeoff has not previously been systematically evaluated. In this paper we propose a methodology for measuring the impact of various degradations (noise, grain, flicker, shake) in a video compression pipeline. We show that noise/grain has the largest impact on codec performance, but that the modern codecs are more robust to the artefact. In addition, we report on the impact of a denoising module deployed as a pre-processor and show that performance metrics change in the context of the pipeline. Denoising would benefit from being treated as part of the processing pipeline both in development and testing.
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