This paper describes a recently created image database, TID2013, intended for evaluation of full-reference visual quality assessment metrics. With respect to TID2008, the new database contains a ...larger number (3000) of test images obtained from 25 reference images, 24 types of distortions for each reference image, and 5 levels for each type of distortion. Motivations for introducing 7 new types of distortions and one additional level of distortions are given; examples of distorted images are presented. Mean opinion scores (MOS) for the new database have been collected by performing 985 subjective experiments with volunteers (observers) from five countries (Finland, France, Italy, Ukraine, and USA). The availability of MOS allows the use of the designed database as a fundamental tool for assessing the effectiveness of visual quality. Furthermore, existing visual quality metrics have been tested with the proposed database and the collected results have been analyzed using rank order correlation coefficients between MOS and considered metrics. These correlation indices have been obtained both considering the full set of distorted images and specific image subsets, for highlighting advantages and drawbacks of existing, state of the art, quality metrics. Approaches to thorough performance analysis for a given metric are presented to detect practical situations or distortion types for which this metric is not adequate enough to human perception. The created image database and the collected MOS values are freely available for downloading and utilization for scientific purposes.
•We have created a new large database.•This database contains larger number of distorted images and distortion types.•MOS values for all images are obtained and provided.•Analysis of correlation between MOS and a wide set of existing metrics is carried out.•Methodology for determining drawbacks of existing visual quality metrics is described.
A ParaBoost Method to Image Quality Assessment Tsung-Jung Liu; Kuan-Hsien Liu; Joe Yuchieh Lin ...
IEEE transaction on neural networks and learning systems,
01/2017, Letnik:
28, Številka:
1
Journal Article
An ensemble method for full-reference image quality assessment (IQA) based on the parallel boosting (ParaBoost) idea is proposed in this paper. We first extract features from existing image quality ...metrics and train them to form basic image quality scorers (BIQSs). Then, we select additional features to address specific distortion types and train them to construct auxiliary image quality scorers (AIQSs). Both BIQSs and AIQSs are trained on small image subsets of certain distortion types and, as a result, they are weak performers with respect to a wide variety of distortions. Finally, we adopt the ParaBoost framework, which is a statistical scorer selection scheme for support vector regression (SVR), to fuse the scores of BIQSs and AIQSs to evaluate the images containing a wide range of distortion types. This ParaBoost methodology can be easily extended to images of new distortion types. Extensive experiments are conducted to demonstrate the superior performance of the ParaBoost method, which outperforms existing IQA methods by a significant margin. Specifically, the Spearman rank order correlation coefficients (SROCCs) of the ParaBoost method with respect to the LIVE, CSIQ, TID2008, and TID2013 image quality databases are 0.98, 0.97, 0.98, and 0.96, respectively.
Picture Wise Just Noticeable Difference (PW-JND), which accounts for the minimum difference of a picture that human visual system can perceive, can be widely used in perception-oriented image and ...video processing. However, the conventional Just Noticeable Difference (JND) models calculate the JND threshold for each pixel or sub-band separately, which may not reflect the total masking effect of a picture accurately. In this paper, we propose a deep learning based PW-JND prediction model for image compression. Firstly, we formulate the task of predicting PW-JND as a multi-class classification problem, and propose a framework to transform the multi-class classification problem to a binary classification problem solved by just one binary classifier. Secondly, we construct a deep learning based binary classifier named perceptually lossy/lossless predictor which can predict whether an image is perceptually lossy to another or not. Finally, we propose a sliding window based search strategy to predict PW-JND based on the prediction results of the perceptually lossy/lossless predictor. Experimental results show that the mean accuracy of the perceptually lossy/lossless predictor reaches 92%, and the absolute prediction error of the proposed PW-JND model is 0.79 dB on average, which show the superiority of the proposed PW-JND model to the conventional JND models.
The just noticeable difference (JND) in an image, which reveals the visibility limitation of the human visual system (HVS), is widely used for visual redundancy estimation in signal processing. To ...determine the JND threshold with the current schemes, the spatial masking effect is estimated as the contrast masking, and this cannot accurately account for the complicated interaction among visual contents. Research on cognitive science indicates that the HVS is highly adapted to extract the repeated patterns for visual content representation. Inspired by this, we formulate the pattern complexity as another factor to determine the total masking effect: the interaction is relatively straightforward with a limited masking effect in a regular pattern, and is complicated with a strong masking effect in an irregular pattern. From the orientation selectivity mechanism in the primary visual cortex, the response of each local receptive field can be considered as a pattern; therefore, in this paper, the orientation that each pixel presents is regarded as the fundamental element of a pattern, and the pattern complexity is calculated as the diversity of the orientation in a local region. Finally, considering both pattern complexity and luminance contrast, a novel spatial masking estimation function is deduced, and an improved JND estimation model is built. Experimental results on comparing with the latest JND models demonstrate the effectiveness of the proposed model, which performs highly consistent with the human perception. The source code of the proposed model is publicly available at http://web.xidian.edu.cn/wjj/en/index.html.
Blood flow and mechanical forces in the ventricle are implicated in cardiac development and trabeculation. However, the mechanisms of mechanotransduction remain elusive. This is due in part to the ...challenges associated with accurately quantifying mechanical forces in the developing heart. We present a novel computational framework to simulate cardiac hemodynamics in developing zebrafish embryos by coupling 4-D light sheet imaging with a stabilized finite element flow solver, and extract time-dependent mechanical stimuli data. We employ deformable image registration methods to segment the motion of the ventricle from high resolution 4-D light sheet image data. This results in a robust and efficient workflow, as segmentation need only be performed at one cardiac phase, while wall position in the other cardiac phases is found by image registration. Ventricular hemodynamics are then quantified by numerically solving the Navier-Stokes equations in the moving wall domain with our validated flow solver. We demonstrate the applicability of the workflow in wild type zebrafish and three treated fish types that disrupt trabeculation: (a) chemical treatment using AG1478, an ErbB2 signaling inhibitor that inhibits proliferation and differentiation of cardiac trabeculation; (b) injection of gata1a morpholino oligomer (gata1aMO) suppressing hematopoiesis and resulting in attenuated trabeculation; (c) weak-atriumm58 mutant (wea) with inhibited atrial contraction leading to a highly undeveloped ventricle and poor cardiac function. Our simulations reveal elevated wall shear stress (WSS) in wild type and AG1478 compared to gata1aMO and wea. High oscillatory shear index (OSI) in the grooves between trabeculae, compared to lower values on the ridges, in the wild type suggest oscillatory forces as a possible regulatory mechanism of cardiac trabeculation development. The framework has broad applicability for future cardiac developmental studies focused on quantitatively investigating the role of hemodynamic forces and mechanotransduction during morphogenesis.
•We propose an FCN-based approach to localize image splicing attacks.•We present three FCN-based approaches (SFCN, MFCN, and edge-enhanced MFCN).•We show that the proposed SFCN and MFCN methods ...outperform many existing methods.
In this work, we propose a technique that utilizes a fully convolutional network (FCN) to localize image splicing attacks. We first evaluated a single-task FCN (SFCN) trained only on the surface label. Although the SFCN is shown to provide superior performance over existing methods, it still provides a coarse localization output in certain cases. Therefore, we propose the use of a multi-task FCN (MFCN) that utilizes two output branches for multi-task learning. One branch is used to learn the surface label, while the other branch is used to learn the edge or boundary of the spliced region. We trained the networks using the CASIA v2.0 dataset, and tested the trained models on the CASIA v1.0, Columbia Uncompressed, Carvalho, and the DARPA/NIST Nimble Challenge 2016 SCI datasets. Experiments show that the SFCN and MFCN outperform existing splicing localization algorithms, and that the MFCN can achieve finer localization than the SFCN.
We studied paediatric patients with human adenovirus (HAdV) infection during the 2011 outbreak in northern Taiwan to define the clinical features of different HAdV genotypes in children.
Between ...January and December 2011, 637 patients <19 years of age exhibited culture-confirmed adenoviral infection in Chang Gung Memorial Hospital, and provided specimens available for genotyping by multiplex real-time PCR. Clinical data were collected retrospectively.
Excluding five cases with multiple genotypes, 632 cases were included for analysis. Three genotypes were identified, including HAdV-3 (429/632; 67.6%), HAdV-7 (144/632; 22.6%) and HAdV-2 (59/632; 9.8%). Median age was 4.58 years (range 2 months to 18 years), with children infected with HAdV-3 significantly older (82.9% >3 years; p <0.001). Of the 621 inpatients, 98.2% had fevers and all exhibited respiratory symptoms, 75 patients (12.1%) had lower respiratory tract infections, 20 (3.2%) required intensive care (HAdV-2: 1; HAdV-3: 8; and HAdV-7: 11), and three died (all HAdV-7-infected). HAdV-3-infected patients were significantly more likely to have upper respiratory symptoms and a high serum C-reactive protein level >100 mg/L, whereas leucocytosis (white blood cell count >15 000/mm3) was more common in HAdV-2-infected patients (p 0.007). HAdV-7 infections were significantly associated with a longer duration of fever, leucopenia (white blood cell count <5000/mm3), thrombocytopenia (platelet count <150 000/mm3), lower respiratory tract infections, a longer length of hospital stay, and requiring intensive care (all p <0.001).
Childhood HAdV-2, HAdV-3 and HAdV-7 infections may exhibit different clinical manifestations. Although HAdV-3 was the most prevalent genotype observed during the 2011 Taiwan outbreak, HAdV-7 caused more severe disease characteristics and outcomes.
The material class of rare earth nickelates with high Ni3+ oxidation state is generating continued interest due to the occurrence of a metal-insulator transition with charge order and the appearance ...of non-collinear magnetic phases within this insulating regime. The recent theoretical prediction for superconductivity in LaNiO3 thin films has also triggered intensive research efforts. LaNiO3 seems to be the only rare earth nickelate that stays metallic and paramagnetic down to lowest temperatures. So far, centimeter-sized impurity-free single crystal growth has not been reported for the rare earth nickelates material class since elevated oxygen pressures are required for their synthesis. Here, we report on the successful growth of centimeter-sized LaNiO3 single crystals by the floating zone technique at oxygen pressures of up to 150 bar. Our crystals are essentially free from Ni2+ impurities and exhibit metallic properties together with an unexpected but clear antiferromagnetic transition.
The multilayer perceptron (MLP) neural network is interpreted from the geometrical viewpoint in this work, that is, an MLP partition an input feature space into multiple nonoverlapping subspaces ...using a set of hyperplanes, where the great majority of samples in a subspace belongs to one object class. Based on this high-level idea, we propose a three-layer feedforward MLP (FF-MLP) architecture for its implementation. In the first layer, the input feature space is split into multiple subspaces by a set of partitioning hyperplanes and rectified linear unit (ReLU) activation, which is implemented by the classical two-class linear discriminant analysis (LDA). In the second layer, each neuron activates one of the subspaces formed by the partitioning hyperplanes with specially designed weights. In the third layer, all subspaces of the same class are connected to an output node that represents the object class. The proposed design determines all MLP parameters in a feedforward one-pass fashion analytically without backpropagation. Experiments are conducted to compare the performance of the traditional backpropagation-based MLP (BP-MLP) and the new FF-MLP. It is observed that the FF-MLP outperforms the BP-MLP in terms of design time, training time, and classification performance in several benchmarking datasets. Our source code is available at https://colab.research.google.com/drive/1Gz0L8AnT4ijrUchrhEXXsnaacrFdenn?usp = sharing .
•Propose a RECOS mathematical model to answer two fundamental questions in convolutional neural networks (CNNs).•The RECOS model interprets operations in CNNs using a rectified correlation ...viewpoint.•The RECOS model justifies the need of nonlinear activation in CNNs.•The RECOS model explains the advantage of two cascaded layers over a single layer in CNNs.•Use the LeNet-5 network applied to the MNIST dataset as an illustrative example.
This work attempts to address two fundamental questions about the structure of the convolutional neural networks (CNN): (1) why a nonlinear activation function is essential at the filter output of all intermediate layers? (2) what is the advantage of the two-layer cascade system over the one-layer system? A mathematical model called the “REctified-COrrelations on a Sphere” (RECOS) is proposed to answer these two questions. After the CNN training process, the converged filter weights define a set of anchor vectors in the RECOS model. Anchor vectors represent the frequently occurring patterns (or the spectral components). The necessity of rectification is explained using the RECOS model. Then, the behavior of a two-layer RECOS system is analyzed and compared with its one-layer counterpart. The LeNet-5 and the MNIST dataset are used to illustrate discussion points. Finally, the RECOS model is generalized to a multilayer system with the AlexNet as an example.
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