39,567 research outputs found
Boosting in Image Quality Assessment
In this paper, we analyze the effect of boosting in image quality assessment
through multi-method fusion. Existing multi-method studies focus on proposing a
single quality estimator. On the contrary, we investigate the generalizability
of multi-method fusion as a framework. In addition to support vector machines
that are commonly used in the multi-method fusion, we propose using neural
networks in the boosting. To span different types of image quality assessment
algorithms, we use quality estimators based on fidelity, perceptually-extended
fidelity, structural similarity, spectral similarity, color, and learning. In
the experiments, we perform k-fold cross validation using the LIVE, the
multiply distorted LIVE, and the TID 2013 databases and the performance of
image quality assessment algorithms are measured via accuracy-, linearity-, and
ranking-based metrics. Based on the experiments, we show that boosting methods
generally improve the performance of image quality assessment and the level of
improvement depends on the type of the boosting algorithm. Our experimental
results also indicate that boosting the worst performing quality estimator with
two or more additional methods leads to statistically significant performance
enhancements independent of the boosting technique and neural network-based
boosting outperforms support vector machine-based boosting when two or more
methods are fused.Comment: Paper: 6 pages, 5 tables, 1 figure, Presentation: 16 slides
[Ancillary files
Multi-measures fusion based on multi-objective genetic programming for full-reference image quality assessment
In this paper, we exploit the flexibility of multi-objective fitness
functions, and the efficiency of the model structure selection ability of a
standard genetic programming (GP) with the parameter estimation power of
classical regression via multi-gene genetic programming (MGGP), to propose a
new fusion technique for image quality assessment (IQA) that is called
Multi-measures Fusion based on Multi-Objective Genetic Programming (MFMOGP).
This technique can automatically select the most significant suitable measures,
from 16 full-reference IQA measures, used in aggregation and finds weights in a
weighted sum of their outputs while simultaneously optimizing for both accuracy
and complexity. The obtained well-performing fusion of IQA measures are
evaluated on four largest publicly available image databases and compared
against state-of-the-art full-reference IQA approaches. Results of comparison
reveal that the proposed approach outperforms other state-of-the-art recently
developed fusion approaches
UNIQUE: Unsupervised Image Quality Estimation
In this paper, we estimate perceived image quality using sparse
representations obtained from generic image databases through an unsupervised
learning approach. A color space transformation, a mean subtraction, and a
whitening operation are used to enhance descriptiveness of images by reducing
spatial redundancy; a linear decoder is used to obtain sparse representations;
and a thresholding stage is used to formulate suppression mechanisms in a
visual system. A linear decoder is trained with 7 GB worth of data, which
corresponds to 100,000 8x8 image patches randomly obtained from nearly 1,000
images in the ImageNet 2013 database. A patch-wise training approach is
preferred to maintain local information. The proposed quality estimator UNIQUE
is tested on the LIVE, the Multiply Distorted LIVE, and the TID 2013 databases
and compared with thirteen quality estimators. Experimental results show that
UNIQUE is generally a top performing quality estimator in terms of accuracy,
consistency, linearity, and monotonic behavior.Comment: 12 pages, 5 figures, 2 table
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
No-Reference Quality Assessment of Contrast-Distorted Images using Contrast Enhancement
No-reference image quality assessment (NR-IQA) aims to measure the image
quality without reference image. However, contrast distortion has been
overlooked in the current research of NR-IQA. In this paper, we propose a very
simple but effective metric for predicting quality of contrast-altered images
based on the fact that a high-contrast image is often more similar to its
contrast enhanced image. Specifically, we first generate an enhanced image
through histogram equalization. We then calculate the similarity of the
original image and the enhanced one by using structural-similarity index (SSIM)
as the first feature. Further, we calculate the histogram based entropy and
cross entropy between the original image and the enhanced one respectively, to
gain a sum of 4 features. Finally, we learn a regression module to fuse the
aforementioned 5 features for inferring the quality score. Experiments on four
publicly available databases validate the superiority and efficiency of the
proposed technique.Comment: Draft versio
A Probabilistic Quality Representation Approach to Deep Blind Image Quality Prediction
Blind image quality assessment (BIQA) remains a very challenging problem due
to the unavailability of a reference image. Deep learning based BIQA methods
have been attracting increasing attention in recent years, yet it remains a
difficult task to train a robust deep BIQA model because of the very limited
number of training samples with human subjective scores. Most existing methods
learn a regression network to minimize the prediction error of a scalar image
quality score. However, such a scheme ignores the fact that an image will
receive divergent subjective scores from different subjects, which cannot be
adequately represented by a single scalar number. This is particularly true on
complex, real-world distorted images. Moreover, images may broadly differ in
their distributions of assigned subjective scores. Recognizing this, we propose
a new representation of perceptual image quality, called probabilistic quality
representation (PQR), to describe the image subjective score distribution,
whereby a more robust loss function can be employed to train a deep BIQA model.
The proposed PQR method is shown to not only speed up the convergence of deep
model training, but to also greatly improve the achievable level of quality
prediction accuracy relative to scalar quality score regression methods. The
source code is available at https://github.com/HuiZeng/BIQA_Toolbox.Comment: Add the link of source cod
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
Predictive No-Reference Assessment of Video Quality
Among the various means to evaluate the quality of video streams,
No-Reference (NR) methods have low computation and may be executed on thin
clients. Thus, NR algorithms would be perfect candidates in cases of real-time
quality assessment, automated quality control and, particularly, in adaptive
mobile streaming. Yet, existing NR approaches are often inaccurate, in
comparison to Full-Reference (FR) algorithms, especially under lossy network
conditions. In this work, we present an NR method that combines machine
learning with simple NR metrics to achieve a quality index comparably as
accurate as the Video Quality Metric (VQM) Full-Reference algorithm. Our method
is tested in an extensive dataset (960 videos), under lossy network conditions
and considering nine different machine learning algorithms. Overall, we achieve
an over 97% correlation with VQM, while allowing real-time assessment of video
quality of experience in realistic streaming scenarios.Comment: 13 pages, 8 figures, IEEE Selected Topics on Signal Processin
Image analysis and statistical modelling for measurement and quality assessment of ornamental horticulture crops in glasshouses
Image analysis for ornamental crops is discussed with examples from the bedding plant industry. Feed-forward artificial neural networks are used to segment top and side view images of three contrasting species of bedding plants. The segmented images provide objective measurements of leaf and flower cover, colour, uniformity and leaf canopy height. On each imaging occasion, each pack was scored for quality by an assessor panel and it is shown that image analysis can explain 88.5%, 81.7% and 70.4% of the panel quality scores for the three species, respectively. Stereoscopy for crop height and uniformity is outlined briefly. The methods discussed here could be used for crop grading at marketing or for monitoring and assessment of growing crops within a glasshouse during all stages of production
A proposal project for a blind image quality assessment by learning distortions from the full reference image quality assessments
This short paper presents a perspective plan to build a null reference image
quality assessment. Its main goal is to deliver both the objective score and
the distortion map for a given distorted image without the knowledge of its
reference image.Comment: International Workshop on Quality of Multimedia Experience, 2012,
Melbourne, Australi
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