91 research outputs found
Selecting Low-level Features for Image Quality Assessment by Statistical Methods
Image quality assessment is animportant component in every image processingsystem where the last link of the chain is thehuman observer. This domain is of increasinginterest, in particular in the context of imagecompression where coding scheme optimizationis based on the distortion measure. Manyobjective image quality measures have beenproposed in the literature and validated bycomparing them to the Mean Opinion Score(MOS). We propose in this paper an empiricalstudy of several indicators and show how onecan improve the performances by combiningthem. We learn a regularized regression modeland apply variable selection techniques toautomatically find the most relevant indicators.Our technique enhances the state of the artresults on two publicly available databases
Predicting blocking effects in the spatial domain using a learning approach
A new method for predicting blocking effect in the spatial domain is proposed. This method aims at estimating the appearance of blocking artefacts in the original image prior to compression for a given bit rate and a given compression technique. The basic idea is to use a training process in order to compute a visibility measure. A weighting function of the blocking effects is then derived from this training process performed on a database. The proposed method is objectively and subjectively evaluated on various actual images. The obtained results confirm the efficiency of the proposed method in predicting blocking effect
CD-COCO: A Versatile Complex Distorted COCO Database for Scene-Context-Aware Computer Vision
The recent development of deep learning methods applied to vision has enabled
their increasing integration into real-world applications to perform complex
Computer Vision (CV) tasks. However, image acquisition conditions have a major
impact on the performance of high-level image processing. A possible solution
to overcome these limitations is to artificially augment the training databases
or to design deep learning models that are robust to signal distortions. We opt
here for the first solution by enriching the database with complex and
realistic distortions which were ignored until now in the existing databases.
To this end, we built a new versatile database derived from the well-known
MS-COCO database to which we applied local and global photo-realistic
distortions. These new local distortions are generated by considering the scene
context of the images that guarantees a high level of photo-realism.
Distortions are generated by exploiting the depth information of the objects in
the scene as well as their semantics. This guarantees a high level of
photo-realism and allows to explore real scenarios ignored in conventional
databases dedicated to various CV applications. Our versatile database offers
an efficient solution to improve the robustness of various CV tasks such as
Object Detection (OD), scene segmentation, and distortion-type classification
methods. The image database, scene classification index, and distortion
generation codes are publicly available
\footnote{\url{https://github.com/Aymanbegh/CD-COCO}
A reduced reference image quality metric based on feature fusion and neural networks
A Global Reduced Reference Image Quality Metric (IQM) based on feature fusion using neural networks is proposed. The main idea is the introduction of a Reduced Reference degradation-dependent IQM (RRIQM/D) across a set of common distortions. The first stage consists of extracting a set of features from the wavelet-based edge map. Such features are then used to identify the type of degradation using Linear Discriminant Analysis (LDA). The second stage consists of fusing the extracted features into a single measure using Artificial Neural Networks (ANN). The result is a degradation- dependent IQM measure called the RRIQM/D. The performance of the proposed method is evaluated using the TID 2008 database and compared to some existing IQMs. The experimental results obtained using the proposed method demonstrate an improved performance even when compared to some Full Reference IQMs
Adaptive Context Encoding Module for Semantic Segmentation
The object sizes in images are diverse, therefore, capturing multiple scale
context information is essential for semantic segmentation. Existing context
aggregation methods such as pyramid pooling module (PPM) and atrous spatial
pyramid pooling (ASPP) design different pooling size or atrous rate, such that
multiple scale information is captured. However, the pooling sizes and atrous
rates are chosen manually and empirically. In order to capture object context
information adaptively, in this paper, we propose an adaptive context encoding
(ACE) module based on deformable convolution operation to argument multiple
scale information. Our ACE module can be embedded into other Convolutional
Neural Networks (CNN) easily for context aggregation. The effectiveness of the
proposed module is demonstrated on Pascal-Context and ADE20K datasets. Although
our proposed ACE only consists of three deformable convolution blocks, it
outperforms PPM and ASPP in terms of mean Intersection of Union (mIoU) on both
datasets. All the experiment study confirms that our proposed module is
effective as compared to the state-of-the-art methods
Residual Networks based Distortion Classification and Ranking for Laparoscopic Image Quality Assessment
Laparoscopic images and videos are often affected by different types of
distortion like noise, smoke, blur and nonuniform illumination. Automatic
detection of these distortions, followed generally by application of
appropriate image quality enhancement methods, is critical to avoid errors
during surgery. In this context, a crucial step involves an objective
assessment of the image quality, which is a two-fold problem requiring both the
classification of the distortion type affecting the image and the estimation of
the severity level of that distortion. Unlike existing image quality measures
which focus mainly on estimating a quality score, we propose in this paper to
formulate the image quality assessment task as a multi-label classification
problem taking into account both the type as well as the severity level (or
rank) of distortions. Here, this problem is then solved by resorting to a deep
neural networks based approach. The obtained results on a laparoscopic image
dataset show the efficiency of the proposed approach.Comment: 5 Pages, ICIP 202
Filtrage perceptuel pyramidal
Dans cet article, un schémas multirésolution perceptuel basé sur le système visuel humain est proposé. L'idée principale est la détection des structures les plus pertinentes de l'image à chaque échelle de la pyramide Gaussienne en utilisant comme critère le seuil de détection en contraste, JNC (pour Just noticeable contrast) et la notion d'adaptation de luminance. Nous appliquons le filtrage perceptuel à des images de niveaux de gris
A Neural Network based Framework for Effective Laparoscopic Video Quality Assessment
Video quality assessment is a challenging problem having a critical
significance in the context of medical imaging. For instance, in laparoscopic
surgery, the acquired video data suffers from different kinds of distortion
that not only hinder surgery performance but also affect the execution of
subsequent tasks in surgical navigation and robotic surgeries. For this reason,
we propose in this paper neural network-based approaches for distortion
classification as well as quality prediction. More precisely, a Residual
Network (ResNet) based approach is firstly developed for simultaneous ranking
and classification task. Then, this architecture is extended to make it
appropriate for the quality prediction task by using an additional Fully
Connected Neural Network (FCNN). To train the overall architecture (ResNet and
FCNN models), transfer learning and end-to-end learning approaches are
investigated. Experimental results, carried out on a new laparoscopic video
quality database, have shown the efficiency of the proposed methods compared to
recent conventional and deep learning based approaches
Can Image Enhancement be Beneficial to Find Smoke Images in Laparoscopic Surgery?
Laparoscopic surgery has a limited field of view. Laser ablation in a
laproscopic surgery causes smoke, which inevitably influences the surgeon's
visibility. Therefore, it is of vital importance to remove the smoke, such that
a clear visualization is possible. In order to employ a desmoking technique,
one needs to know beforehand if the image contains smoke or not, to this date,
there exists no accurate method that could classify the smoke/non-smoke images
completely. In this work, we propose a new enhancement method which enhances
the informative details in the RGB images for discrimination of smoke/non-smoke
images. Our proposed method utilizes weighted least squares optimization
framework~(WLS). For feature extraction, we use statistical features based on
bivariate histogram distribution of gradient magnitude~(GM) and Laplacian of
Gaussian~(LoG). We then train a SVM classifier with binary smoke/non-smoke
classification task. We demonstrate the effectiveness of our method on Cholec80
dataset. Experiments using our proposed enhancement method show promising
results with improvements of 4\% in accuracy and 4\% in F1-Score over the
baseline performance of RGB images. In addition, our approach improves over the
saturation histogram based classification methodologies Saturation
Analysis~(SAN) and Saturation Peak Analysis~(SPA) by 1/5\% and 1/6\% in
accuracy/F1-Score metrics.Comment: In proceedings of IST, Color and Imaging Conference (CIC 26).
Congcong Wang and Vivek Sharma contributed equally to this work and listed in
alphabetical orde
- …