Regression or Classification? New Methods to Evaluate No-Reference Picture and Video Quality Models
This work addresses the problem of more tractable and practical quality assessment for user-generated content processing, though it is incremental in shifting evaluation methods.
The paper tackles the challenge of evaluating no-reference picture and video quality models by proposing binary and ordinal classification as alternatives to regression, and provides benchmark results on real-world user-generated content datasets.
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.