102,217 research outputs found
Learning to Rank for Blind Image Quality Assessment
Blind image quality assessment (BIQA) aims to predict perceptual image
quality scores without access to reference images. State-of-the-art BIQA
methods typically require subjects to score a large number of images to train a
robust model. However, subjective quality scores are imprecise, biased, and
inconsistent, and it is challenging to obtain a large scale database, or to
extend existing databases, because of the inconvenience of collecting images,
training the subjects, conducting subjective experiments, and realigning human
quality evaluations. To combat these limitations, this paper explores and
exploits preference image pairs (PIPs) such as "the quality of image is
better than that of image " for training a robust BIQA model. The
preference label, representing the relative quality of two images, is generally
precise and consistent, and is not sensitive to image content, distortion type,
or subject identity; such PIPs can be generated at very low cost. The proposed
BIQA method is one of learning to rank. We first formulate the problem of
learning the mapping from the image features to the preference label as one of
classification. In particular, we investigate the utilization of a multiple
kernel learning algorithm based on group lasso (MKLGL) to provide a solution. A
simple but effective strategy to estimate perceptual image quality scores is
then presented. Experiments show that the proposed BIQA method is highly
effective and achieves comparable performance to state-of-the-art BIQA
algorithms. Moreover, the proposed method can be easily extended to new
distortion categories
Efficient No-Reference Quality Assessment and Classification Model for Contrast Distorted Images
In this paper, an efficient Minkowski Distance based Metric (MDM) for
no-reference (NR) quality assessment of contrast distorted images is proposed.
It is shown that higher orders of Minkowski distance and entropy provide
accurate quality prediction for the contrast distorted images. The proposed
metric performs predictions by extracting only three features from the
distorted images followed by a regression analysis. Furthermore, the proposed
features are able to classify type of the contrast distorted images with a high
accuracy. Experimental results on four datasets CSIQ, TID2013, CCID2014, and
SIQAD show that the proposed metric with a very low complexity provides better
quality predictions than the state-of-the-art NR metrics. The MATLAB source
code of the proposed metric is available to public at
http://www.synchromedia.ca/system/files/MDM.zip.Comment: 6 pages, 4 figures, 4 table
Blind Predicting Similar Quality Map for Image Quality Assessment
A key problem in blind image quality assessment (BIQA) is how to effectively
model the properties of human visual system in a data-driven manner. In this
paper, we propose a simple and efficient BIQA model based on a novel framework
which consists of a fully convolutional neural network (FCNN) and a pooling
network to solve this problem. In principle, FCNN is capable of predicting a
pixel-by-pixel similar quality map only from a distorted image by using the
intermediate similarity maps derived from conventional full-reference image
quality assessment methods. The predicted pixel-by-pixel quality maps have good
consistency with the distortion correlations between the reference and
distorted images. Finally, a deep pooling network regresses the quality map
into a score. Experiments have demonstrated that our predictions outperform
many state-of-the-art BIQA methods
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
Assessing the Sharpness of Satellite Images: Study of the PlanetScope Constellation
New micro-satellite constellations enable unprecedented systematic monitoring
applications thanks to their wide coverage and short revisit capabilities.
However, the large volumes of images that they produce have uneven qualities,
creating the need for automatic quality assessment methods. In this work, we
quantify the sharpness of images from the PlanetScope constellation by
estimating the blur kernel from each image. Once the kernel has been estimated,
it is possible to compute an absolute measure of sharpness which allows to
discard low quality images and deconvolve blurry images before any further
processing. The method is fully blind and automatic, and since it does not
require the knowledge of any satellite specifications it can be ported to other
constellations.Comment: Accepted at IGARSS 201
Learn to Evaluate Image Perceptual Quality Blindly from Statistics of Self-similarity
Among the various image quality assessment (IQA) tasks, blind IQA (BIQA) is
particularly challenging due to the absence of knowledge about the reference
image and distortion type. Features based on natural scene statistics (NSS)
have been successfully used in BIQA, while the quality relevance of the feature
plays an essential role to the quality prediction performance. Motivated by the
fact that the early processing stage in human visual system aims to remove the
signal redundancies for efficient visual coding, we propose a simple but very
effective BIQA method by computing the statistics of self-similarity (SOS) in
an image. Specifically, we calculate the inter-scale similarity and intra-scale
similarity of the distorted image, extract the SOS features from these
similarities, and learn a regression model to map the SOS features to the
subjective quality score. Extensive experiments demonstrate very competitive
quality prediction performance and generalization ability of the proposed SOS
based BIQA method
dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs
Objective assessment of image quality is fundamentally important in many
image processing tasks. In this work, we focus on learning blind image quality
assessment (BIQA) models which predict the quality of a digital image with no
access to its original pristine-quality counterpart as reference. One of the
biggest challenges in learning BIQA models is the conflict between the gigantic
image space (which is in the dimension of the number of image pixels) and the
extremely limited reliable ground truth data for training. Such data are
typically collected via subjective testing, which is cumbersome, slow, and
expensive. Here we first show that a vast amount of reliable training data in
the form of quality-discriminable image pairs (DIP) can be obtained
automatically at low cost by exploiting large-scale databases with diverse
image content. We then learn an opinion-unaware BIQA (OU-BIQA, meaning that no
subjective opinions are used for training) model using RankNet, a pairwise
learning-to-rank (L2R) algorithm, from millions of DIPs, each associated with a
perceptual uncertainty level, leading to a DIP inferred quality (dipIQ) index.
Extensive experiments on four benchmark IQA databases demonstrate that dipIQ
outperforms state-of-the-art OU-BIQA models. The robustness of dipIQ is also
significantly improved as confirmed by the group MAximum Differentiation (gMAD)
competition method. Furthermore, we extend the proposed framework by learning
models with ListNet (a listwise L2R algorithm) on quality-discriminable image
lists (DIL). The resulting DIL Inferred Quality (dilIQ) index achieves an
additional performance gain
No-Reference Color Image Quality Assessment: From Entropy to Perceptual Quality
This paper presents a high-performance general-purpose no-reference (NR)
image quality assessment (IQA) method based on image entropy. The image
features are extracted from two domains. In the spatial domain, the mutual
information between the color channels and the two-dimensional entropy are
calculated. In the frequency domain, the two-dimensional entropy and the mutual
information of the filtered sub-band images are computed as the feature set of
the input color image. Then, with all the extracted features, the support
vector classifier (SVC) for distortion classification and support vector
regression (SVR) are utilized for the quality prediction, to obtain the final
quality assessment score. The proposed method, which we call entropy-based
no-reference image quality assessment (ENIQA), can assess the quality of
different categories of distorted images, and has a low complexity. The
proposed ENIQA method was assessed on the LIVE and TID2013 databases and showed
a superior performance. The experimental results confirmed that the proposed
ENIQA method has a high consistency of objective and subjective assessment on
color images, which indicates the good overall performance and generalization
ability of ENIQA. The source code is available on github
https://github.com/jacob6/ENIQA.Comment: 12 pages, 8 figure
UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content
Recent years have witnessed an explosion of user-generated content (UGC)
videos shared and streamed over the Internet, thanks to the evolution of
affordable and reliable consumer capture devices, and the tremendous popularity
of social media platforms. Accordingly, there is a great need for accurate
video quality assessment (VQA) models for UGC/consumer videos to monitor,
control, and optimize this vast content. Blind quality prediction of
in-the-wild videos is quite challenging, since the quality degradations of UGC
content are unpredictable, complicated, and often commingled. Here we
contribute to advancing the UGC-VQA problem by conducting a comprehensive
evaluation of leading no-reference/blind VQA (BVQA) features and models on a
fixed evaluation architecture, yielding new empirical insights on both
subjective video quality studies and VQA model design. By employing a feature
selection strategy on top of leading VQA model features, we are able to extract
60 of the 763 statistical features used by the leading models to create a new
fusion-based BVQA model, which we dub the \textbf{VID}eo quality
\textbf{EVAL}uator (VIDEVAL), that effectively balances the trade-off between
VQA performance and efficiency. Our experimental results show that VIDEVAL
achieves state-of-the-art performance at considerably lower computational cost
than other leading models. Our study protocol also defines a reliable benchmark
for the UGC-VQA problem, which we believe will facilitate further research on
deep learning-based VQA modeling, as well as perceptually-optimized efficient
UGC video processing, transcoding, and streaming. To promote reproducible
research and public evaluation, an implementation of VIDEVAL has been made
available online: \url{https://github.com/tu184044109/VIDEVAL_release}.Comment: 13 pages, 11 figures, 11 table
NIMA: Neural Image Assessment
Automatically learned quality assessment for images has recently become a hot
topic due to its usefulness in a wide variety of applications such as
evaluating image capture pipelines, storage techniques and sharing media.
Despite the subjective nature of this problem, most existing methods only
predict the mean opinion score provided by datasets such as AVA [1] and TID2013
[2]. Our approach differs from others in that we predict the distribution of
human opinion scores using a convolutional neural network. Our architecture
also has the advantage of being significantly simpler than other methods with
comparable performance. Our proposed approach relies on the success (and
retraining) of proven, state-of-the-art deep object recognition networks. Our
resulting network can be used to not only score images reliably and with high
correlation to human perception, but also to assist with adaptation and
optimization of photo editing/enhancement algorithms in a photographic
pipeline. All this is done without need for a "golden" reference image,
consequently allowing for single-image, semantic- and perceptually-aware,
no-reference quality assessment.Comment: IEEE Transactions on Image Processing 201
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