34,924 research outputs found
Blind Quality Assessment for in-the-Wild Images via Hierarchical Feature Fusion and Iterative Mixed Database Training
Image quality assessment (IQA) is very important for both end-users and
service-providers since a high-quality image can significantly improve the
user's quality of experience (QoE) and also benefit lots of computer vision
algorithms. Most existing blind image quality assessment (BIQA) models were
developed for synthetically distorted images, however, they perform poorly on
in-the-wild images, which are widely existed in various practical applications.
In this paper, we propose a novel BIQA model for in-the-wild images by
addressing two critical problems in this field: how to learn better
quality-aware feature representation, and how to solve the problem of
insufficient training samples in terms of their content and distortion
diversity. Considering that perceptual visual quality is affected by both
low-level visual features (e.g. distortions) and high-level semantic
information (e.g. content), we first propose a staircase structure to
hierarchically integrate the features from intermediate layers into the final
feature representation, which enables the model to make full use of visual
information from low-level to high-level. Then an iterative mixed database
training (IMDT) strategy is proposed to train the BIQA model on multiple
databases simultaneously, so the model can benefit from the increase in both
training samples and image content and distortion diversity and can learn a
more general feature representation. Experimental results show that the
proposed model outperforms other state-of-the-art BIQA models on six
in-the-wild IQA databases by a large margin. Moreover, the proposed model shows
an excellent performance in the cross-database evaluation experiments, which
further demonstrates that the learned feature representation is robust to
images with diverse distortions and content. The code will be released publicly
for reproducible research
Regression-free Blind Image Quality Assessment
Regression-based blind image quality assessment (IQA) models are susceptible
to biased training samples, leading to a biased estimation of model parameters.
To mitigate this issue, we propose a regression-free framework for image
quality evaluation, which is founded upon retrieving similar instances by
incorporating semantic and distortion features. The motivation behind this
approach is rooted in the observation that the human visual system (HVS) has
analogous visual responses to semantically similar image contents degraded by
the same distortion. The proposed framework comprises two classification-based
modules: semantic-based classification (SC) module and distortion-based
classification (DC) module. Given a test image and an IQA database, the SC
module retrieves multiple pristine images based on semantic similarity. The DC
module then retrieves instances based on distortion similarity from the
distorted images that correspond to each retrieved pristine image. Finally, the
predicted quality score is derived by aggregating the subjective quality scores
of multiple retrieved instances. Experimental results on four benchmark
databases validate that the proposed model can remarkably outperform the
state-of-the-art regression-based models.Comment: 11 pages, 7 figures, 50 conference
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