16,664 research outputs found
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
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
Terahertz Security Image Quality Assessment by No-reference Model Observers
To provide the possibility of developing objective image quality assessment
(IQA) algorithms for THz security images, we constructed the THz security image
database (THSID) including a total of 181 THz security images with the
resolution of 127*380. The main distortion types in THz security images were
first analyzed for the design of subjective evaluation criteria to acquire the
mean opinion scores. Subsequently, the existing no-reference IQA algorithms,
which were 5 opinion-aware approaches viz., NFERM, GMLF, DIIVINE, BRISQUE and
BLIINDS2, and 8 opinion-unaware approaches viz., QAC, SISBLIM, NIQE, FISBLIM,
CPBD, S3 and Fish_bb, were executed for the evaluation of the THz security
image quality. The statistical results demonstrated the superiority of Fish_bb
over the other testing IQA approaches for assessing the THz image quality with
PLCC (SROCC) values of 0.8925 (-0.8706), and with RMSE value of 0.3993. The
linear regression analysis and Bland-Altman plot further verified that the
Fish__bb could substitute for the subjective IQA. Nonetheless, for the
classification of THz security images, we tended to use S3 as a criterion for
ranking THz security image grades because of the relatively low false positive
rate in classifying bad THz image quality into acceptable category (24.69%).
Interestingly, due to the specific property of THz image, the average pixel
intensity gave the best performance than the above complicated IQA algorithms,
with the PLCC, SROCC and RMSE of 0.9001, -0.8800 and 0.3857, respectively. This
study will help the users such as researchers or security staffs to obtain the
THz security images of good quality. Currently, our research group is
attempting to make this research more comprehensive.Comment: 13 pages, 8 figures, 4 table
Compressed Image Quality Assessment Based on Saak Features
Compressed image quality assessment plays an important role in image
services, especially in image compression applications, which can be utilized
as a guidance to optimize image processing algorithms. In this paper, we
propose an objective image quality assessment algorithm to measure the quality
of compressed images. The proposed method utilizes a data-driven transform,
Saak (Subspace approximation with augmented kernels), to decompose images into
hierarchical structural feature space. We measure the distortions of Saak
features and accumulate these distortions according to the feature importance
to human visual system. Compared with the state-of-the-art image quality
assessment methods on widely utilized datasets, the proposed method correlates
better with the subjective results. In addition, the proposed methods achieves
more robust results on different datasets
Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
We present a deep neural network-based approach to image quality assessment
(IQA). The network is trained end-to-end and comprises ten convolutional layers
and five pooling layers for feature extraction, and two fully connected layers
for regression, which makes it significantly deeper than related IQA models.
Unique features of the proposed architecture are that: 1) with slight
adaptations it can be used in a no-reference (NR) as well as in a
full-reference (FR) IQA setting and 2) it allows for joint learning of local
quality and local weights, i.e., relative importance of local quality to the
global quality estimate, in an unified framework. Our approach is purely
data-driven and does not rely on hand-crafted features or other types of prior
domain knowledge about the human visual system or image statistics. We evaluate
the proposed approach on the LIVE, CISQ, and TID2013 databases as well as the
LIVE In the wild image quality challenge database and show superior performance
to state-of-the-art NR and FR IQA methods. Finally, cross-database evaluation
shows a high ability to generalize between different databases, indicating a
high robustness of the learned features
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
SAR Image Despeckling Algorithms using Stochastic Distances and Nonlocal Means
This paper presents two approaches for filter design based on stochastic
distances for intensity speckle reduction. A window is defined around each
pixel, overlapping samples are compared and only those which pass a
goodness-of-fit test are used to compute the filtered value. The tests stem
from stochastic divergences within the Information Theory framework. The
technique is applied to intensity Synthetic Aperture Radar (SAR) data with
homogeneous regions using the Gamma model. The first approach uses a
Nagao-Matsuyama-type procedure for setting the overlapping samples, and the
second uses the nonlocal method. The proposals are compared with the Improved
Sigma filter and with anisotropic diffusion for speckled data (SRAD) using a
protocol based on Monte Carlo simulation. Among the criteria used to quantify
the quality of filters, we employ the equivalent number of looks, and line and
edge preservation. Moreover, we also assessed the filters by the Universal
Image Quality Index and by the Pearson correlation between edges. Applications
to real images are also discussed. The proposed methods show good results.Comment: Accepted for publication in Workshop of Theses and Dissertations
(WTD) in Conference on Graphics, Patterns, and Images (SIBGRAPI 2013). This
paper received the first best work award in the Dissertation category at the
WTD-SIBGRAP
Contrast Enhancement of Brightness-Distorted Images by Improved Adaptive Gamma Correction
As an efficient image contrast enhancement (CE) tool, adaptive gamma
correction (AGC) was previously proposed by relating gamma parameter with
cumulative distribution function (CDF) of the pixel gray levels within an
image. ACG deals well with most dimmed images, but fails for globally bright
images and the dimmed images with local bright regions. Such two categories of
brightness-distorted images are universal in real scenarios, such as improper
exposure and white object regions. In order to attenuate such deficiencies,
here we propose an improved AGC algorithm. The novel strategy of negative
images is used to realize CE of the bright images, and the gamma correction
modulated by truncated CDF is employed to enhance the dimmed ones. As such,
local over-enhancement and structure distortion can be alleviated. Both
qualitative and quantitative experimental results show that our proposed method
yields consistently good CE results
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
Natural Color Image Enhancement based on Modified Multiscale Retinex Algorithm and Performance Evaluation usingWavelet Energy
This paper presents a new color image enhancement technique based on modified
MultiScale Retinex(MSR) algorithm and visual quality of the enhanced images are
evaluated using a new metric, namely, wavelet energy. The color image
enhancement is achieved by down sampling the value component of HSV color space
converted image into three scales (normal, medium and fine) following the
contrast stretching operation. These down sampled value components are enhanced
using the MSR algorithm. The value component is reconstructed by averaging each
pixels of the lower scale image with that of the upper scale image subsequent
to up sampling the lower scale image. This process replaces dark pixel by the
average pixels of both the lower scale and upper scale, while retaining the
bright pixels. The quality of the reconstructed images in the proposed method
is found to be good and far better then the other researchers method. The
performance of the proposed scheme is evaluated using new wavelet domain based
assessment criterion, referred as wavelet energy. This scheme computes the
energy of both original and enhanced image in wavelet domain. The number of
edge details as well as wavelet energy is less in a poor quality image compared
with naturally enhanced image. Experimental results presented confirms that the
proposed wavelet energy based color image quality assessment technique
efficiently characterizes both the local and global details of enhanced image.Comment: 10 pages, 3 figures, Recent Advances in Intelligent Informatics
Advances in Intelligent Systems and Computing Volume 235, 2014, pp 83-9
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