Regression or Classification? New Methods to Evaluate No-Reference Picture and Video Quality Models

• URL: http://arxiv.org/abs/2102.00155v1
• Date: Sat, 30 Jan 2021 05:40:14 GMT
• Title: Regression or Classification? New Methods to Evaluate No-Reference Picture and Video Quality Models
• Authors: Zhengzhong Tu, Chia-Ju Chen, Li-Heng Chen, Yilin Wang, Neil Birkbeck, Balu Adsumilli, and Alan C. Bovik
• Abstract summary: We propose two new methods to evaluate and compare no-reference quality models at coarser levels. We conduct a benchmark experiment of popular no-reference quality models on recent in-the-wild picture and video quality datasets.
• Score: 45.974399400141685
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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