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Regression Or Classification? New Methods To Evaluate No-Reference Picture And Video Quality Models

Zhengzhong Tu, Chia-Ju Chen, Li-Heng Chen, Yilin Wang, Neil Birkbeck, Balu Adsumilli, Alan Bovik

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    Length: 00:11:35
10 Jun 2021

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.

Chairs:
Chaker Larabi

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