2 research outputs found
Deep Optimization model for Screen Content Image Quality Assessment using Neural Networks
In this paper, we propose a novel quadratic optimized model based on the deep
convolutional neural network (QODCNN) for full-reference and no-reference
screen content image (SCI) quality assessment. Unlike traditional CNN methods
taking all image patches as training data and using average quality pooling,
our model is optimized to obtain a more effective model including three steps.
In the first step, an end-to-end deep CNN is trained to preliminarily predict
the image visual quality, and batch normalized (BN) layers and l2
regularization are employed to improve the speed and performance of network
fitting. For second step, the pretrained model is fine-tuned to achieve better
performance under analysis of the raw training data. An adaptive weighting
method is proposed in the third step to fuse local quality inspired by the
perceptual property of the human visual system (HVS) that the HVS is sensitive
to image patches containing texture and edge information. The novelty of our
algorithm can be concluded as follows: 1) with the consideration of correlation
between local quality and subjective differential mean opinion score (DMOS),
the Euclidean distance is utilized to measure effectiveness of image patches,
and the pretrained model is fine-tuned with more effective training data; 2) an
adaptive pooling approach is employed to fuse patch quality of textual and
pictorial regions, whose feature only extracted from distorted images owns
strong noise robust and effects on both FR and NR IQA; 3) Considering the
characteristics of SCIs, a deep and valid network architecture is designed for
both NR and FR visual quality evaluation of SCIs. Experimental results verify
that our model outperforms both current no-reference and full-reference image
quality assessment methods on the benchmark screen content image quality
assessment database (SIQAD).Comment: 12pages, 9 figure
Full Reference Screen Content Image Quality Assessment by Fusing Multi-level Structure Similarity
The screen content images (SCIs) usually comprise various content types with
sharp edges, in which the artifacts or distortions can be well sensed by the
vanilla structure similarity measurement in a full reference manner.
Nonetheless, almost all of the current SOTA structure similarity metrics are
"locally" formulated in a single-level manner, while the true human visual
system (HVS) follows the multi-level manner, and such mismatch could eventually
prevent these metrics from achieving trustworthy quality assessment. To
ameliorate, this paper advocates a novel solution to measure structure
similarity "globally" from the perspective of sparse representation. To perform
multi-level quality assessment in accordance with the real HVS, the
above-mentioned global metric will be integrated with the conventional local
ones by resorting to the newly devised selective deep fusion network. To
validate its efficacy and effectiveness, we have compared our method with 12
SOTA methods over two widely-used large-scale public SCI datasets, and the
quantitative results indicate that our method yields significantly higher
consistency with subjective quality score than the currently leading works.
Both the source code and data are also publicly available to gain widespread
acceptance and facilitate new advancement and its validation