Citation: Jing, W.; Bai, Y.; Zhu, Z.;
Zhang, R.; Jin, Y. Dual-Anchor Metric
Learning for Blind Image Quality
Assessment of Screen Content
Images. Electronics 2022, 11, 2510.
https://doi.org/10.3390/
electronics11162510
Academic Editor: Silvia
Liberata Ullo
Received: 29 June 2022
Accepted: 8 August 2022
Published: 11 August 2022
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Article
Dual-Anchor Metric Learning for Blind Image Quality
Assessment of Screen Content Images
Weiyi Jing, Yongqiang Bai *, Zhongjie Zhu *, Rong Zhang and Yiwen Jin
Ningbo Key Lab of DSP, Zhejiang Wanli University, Ningbo 315000, China
*
Correspondence: yongqiangbai@zwu.edu.cn (Y.B.); zhongjiezhu@yeah.net (Z.Z.); Tel.: +86-150-5842-9576 (Y.B.);
+86-137-7700-3378 (Z.Z.)
Abstract:
The natural scene statistic is destroyed by the artificial portion in the screen content images
(SCIs) and is also impractical for obtaining an accurate statistical model due to the variable composition
of the artificial and natural parts in SCIs. To resolve this problem, this paper presents a dual-anchor
metric learning (DAML) method that is inspired by metric learning to obtain discriminative statistical
features and further identify complex distortions, as well as predict SCI image quality. First, two
Gaussian mixed models with prior data are constructed as the target anchors of the statistical model
from natural and artificial image databases, which can effectively enhance the metrical discrimination
of the mapping relation between the feature representation and quality degradation by conditional
probability analysis. Then, the distances of the high-order statistics are softly aggregated to conduct
metric learning between the local features and clusters of each target statistical model. Through
empirical analysis and experimental verification, only variance differences are used as quality-aware
features to benefit the balance of complexity and effectiveness. Finally, the mapping model between
the target distances and subjective quality can be obtained by support vector regression. To validate the
performance of DAML, multiple experiments are carried out on three public databases: SIQAD, SCD,
and SCID. Meanwhile, PLCC, SRCC, and the RMSE are then employed to compute the correlation
between subjective and objective ratings, which can estimate the prediction of accuracy, monotonicity,
and consistency, respectively. The PLCC and RMSE of the method achieved 0.9136 and 0.7993. The
results confirm the good performance of the proposed method.
Keywords:
blind image quality assessment; screen content image; metric learning; Gaussian
mixture model
1. Introduction
The screen content image (SCI) is an important medium for human–computer interac-
tion that can offer people a high standard of comfort and high-quality visual experiences.
Thus, SCIs are extensively used in remote desktops, cloud computing, video games, multi-
screen interaction, and other fields [
1
–
4
]. However, a great deal of noise will inevitably
be involved in the process of image acquirement, transmission, and storage, which can
lead to SCI image quality degradation and decrease people’s visual experience [
5
–
7
]. Thus,
a reliable estimation of SCIs plays a critical role in the optimization of processing systems
as guidance. Currently, image quality assessment (IQA) methods can be classified into
three categories: full-reference (FR), reduced-reference (RR), and no-reference or blind (NR),
based on the existence of reference image information. However, because the reference
version of authentically distorted images is not available in most cases, constructing an
effective blind image quality assessment (BIQA) method for SCIs has important research
significance and practical application value.
1.1. Related Work
Many BIQA methods have progressed markedly in recent decades when analyzing
natural images. However, these methods are not suitable for SCIs, as demonstrated in
Electronics 2022, 11, 2510. https://doi.org/10.3390/electronics11162510 https://www.mdpi.com/journal/electronics
