Video and image quality assessment has long been projected as a regression problem, which requires predicting a continuous quality score given an input stimulus. However, recent efforts have shown that accurate quality score regression on real-world user-generated content (UGC) is a very challenging task. To make the problem more tractable, we propose two new methods - binary, and ordinal classification - as alternatives to evaluate and compare no-reference quality models at coarser levels. Moreover, the proposed new tasks convey more practical meaning on perceptually optimized UGC transcoding, or for preprocessing on media processing platforms. We conduct a comprehensive benchmark experiment of popular no-reference quality models on recent in-the-wild picture and video quality datasets, providing reliable baselines for both evaluation methods to support further studies. We hope this work promotes coarse-grained perceptual modeling and its applications to efficient UGC processing.
@article{arxiv.2102.00155,
title = {Regression or Classification? New Methods to Evaluate No-Reference Picture and Video Quality Models},
author = {Zhengzhong Tu and Chia-Ju Chen and Li-Heng Chen and Yilin Wang and Neil Birkbeck and Balu Adsumilli and Alan C. Bovik},
journal= {arXiv preprint arXiv:2102.00155},
year = {2021}
}