Towards Perceptually-Optimized Compression Of User Generated Content (UGC): Prediction Of UGC Rate-Distortion Category

摘要

How to best evaluate the perceptual quality, and efficiently optimize the compression of User Generated Content (UGC) within an adaptive streaming system is becoming one of the most intractable challenges in the community. Rate-Distortion (R-D) characteristic based content analyses, which could be applied on the non-pristine originals, is inevitable to provide guidance in developing quality metrics and efficient compression system. To this end, we present a novel complete R-D category prediction system through the identification of discriminate features. To better understand the Rate-Distortion (R-D) behaviors of UGC, we first propose a Bjontegaard Delta (BD)-Rate, BD-Quality-based algorithm to categorize UGC. By using the predicted R-D related categories as ground-truth labels, we further identify features that characterize the R-D behaviors of UGC via a hierarchical feature selection framework. Finally, selected features are employed to predict the R-D category of under-test UGC. Comprehensive observations and results are summarized through extensive experiments.