Paper Info
Title
REGRESSION OR CLASSIFICATION? NEW METHODS TO EVALUATE NO-REFERENCE PICTURE AND VIDEO QUALITY MODELS
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.
Year
DOI
Venue
2021
10.1109/ICASSP39728.2021.9414232
2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021)
Keywords
DocType
Citations 
Video quality assessment, image quality assessment, user-generated content, classification
Conference
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Zhengzhong Tu101.35
Chen Chia-Ju252.10
Li-Heng Chen3252.74
Yilin Wang433.15
Neil Birkbeck514116.44
Balu Adsumilli6168.19
Alan C. Bovik75062349.55