Paper Info
Title
Comparative Perceptual Assessment of Visual Signals Using Free Energy Features
Abstract
In this paper, we put forward the concept of comparative perceptual quality assessment (C-PQA), which refers to the judgment of relative qualities of two visual signals of the same content, but subject to different types and levels of distortions. While it is straightforward for human observers to fulfill the CPQA task in daily lives, it remains a difficult challenge for the current research of perceptual quality assessment (PQA). Among the existing PQA algorithms, the full-reference (FR) and reducedreference (RR) methods both need prior knowledge of the original images while the no-reference (NR) algorithms usually work with a single input image. C-PQA is inherently different from those existing methods in that it takes an image pair as input and predicts their relative quality without using any knowledge about the original image. In this paper, we propose a brain theory inspired approach to C-PQA that emulates the process of comparing the relative quality of two visual stimuli as performed by the human visual system (HVS) within the framework of free energy minimization. The brain's internal generative models initialized on the inputs are then used to explain both images. During the internal generative modeling, a group of features are extracted and then integrated to determine the relative quality of two images. We designed a dedicated image database to test the proposed C-PQA algorithm. Experimental results show that the proposed method achieves up to 98% prediction accuracy in line with the subjective ratings, outperforming many state of the art PQA algorithms.
Year
DOI
Venue
2021
10.1109/TMM.2020.3029891
IEEE TRANSACTIONS ON MULTIMEDIA
Keywords
DocType
Volume
Distortion, Visualization, Prediction algorithms, Quality assessment, Brain modeling, Distortion measurement, Computational modeling, Perceptual quality assessment, full-reference quality assessment, no-reference quality assessment, comparative quality assessment, free energy, autoregressive model
Journal
23
ISSN
Citations 
PageRank 
1520-9210
0
0.34
References 
Authors
46
3
Name
Order
Citations
PageRank
Guangtao Zhai11707145.33
Yucheng Zhu2355.71
Xiongkuo Min333740.88