Since human observers are the ultimate receivers of digital images, image quality metrics should be designed from a human-oriented perspective. Conventionally, a number of full-reference image quality assessment (FR-IQA) methods adopted various computational models of the human visual system (HVS) from psychological vision science research. In this paper, we propose a novel convolutional neural networks (CNN) based FR-IQA model, named Deep Image Quality Assessment (DeepQA), where the behavior of the HVS is learned from the underlying data distribution of IQA databases. Different from previous studies, our model seeks the optimal visual weight based on understanding of database information itself without any prior knowledge of the HVS. Through the experiments, we show that the predicted visual sensitivity maps agree with the human subjective opinions. In addition, DeepQA achieves the state-of-the-art prediction accuracy among FR-IQA models.

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Deep Learning of Human Visual Sensitivity in Image Quality Assessment Framework

This paper presents an improved fruit fly optimization (IFFO) algorithm for solving continuous function optimization problems. In the proposed IFFO, a new control parameter is introduced to tune the search scope around its swarm location adaptively. A new solution generating method is developed to enhance accuracy and convergence rate of the algorithm. Extensive computational experiments and comparisons are carried out based on a set of 29 benchmark functions from the literature. The computational results show that the proposed IFFO not only significantly improves the basic fruit fly optimization algorithm but also performs much better than five state-of-the-art harmony search algorithms.

An improved fruit fly optimization algorithm for continuous function optimization problems

In this paper, a novel binary fruit fly optimization algorithm (bFOA) is proposed to solve the multidimensional knapsack problem (MKP). In the bFOA, binary string is used to represent the solution of the MKP, and three main search processes are designed to perform evolutionary search, including smell-based search process, local vision-based search process and global vision-based search process. In particular, a group generating probability vector is designed for producing new solutions. To enhance the exploration ability, a global vision mechanism based on differential information among fruit flies is proposed to update the probability vector. Meanwhile, two repair operators are employed to guarantee the feasibility of solutions. The influence of the parameter setting is investigated based on the Taguchi method of design of experiment. Extensive numerical testing results based on benchmark instances are provided. And the comparisons to the existing algorithms demonstrate the effectiveness of the proposed bFOA in solving the MKP, especially for the large-scale problems.

A novel binary fruit fly optimization algorithm for solving the multidimensional knapsack problem

Display Omitted Development of new method named chaotic fruit fly optimization algorithm (CFOA).Fruit fly algorithm (FOA) is integrated with ten different chaos maps.Novel algorithm is tested on ten different well known benchmark problems.CFOA is compared with FOA, FOA with Levy distribution, and similar chaotic methods.Experiments show superiority of CFOA in terms of obtained statistical results. Fruit fly optimization algorithm (FOA) is recently presented metaheuristic technique that is inspired by the behavior of fruit flies. This paper improves the standard FOA by introducing the novel parameter integrated with chaos. The performance of developed chaotic fruit fly algorithm (CFOA) is investigated in details on ten well known benchmark problems using fourteen different chaotic maps. Moreover, we performed comparison studies with basic FOA, FOA with Levy flight distribution, and other recently published chaotic algorithms. Statistical results on every optimization task indicate that the chaotic fruit fly algorithm (CFOA) has a very fast convergence rate. In addition, CFOA is compared with recently developed chaos enhanced algorithms such as chaotic bat algorithm, chaotic accelerated particle swarm optimization, chaotic firefly algorithm, chaotic artificial bee colony algorithm, and chaotic cuckoo search. Overall research findings show that FOA with Chebyshev map show superiority in terms of reliability of global optimality and algorithm success rate.

Chaotic fruit fly optimization algorithm

The ability to estimate the perceptual error between images is an important problem in computer vision with many applications. Although it has been studied extensively, however, no method currently exists that can robustly predict visual differences like humans. Some previous approaches used hand-coded models, but they fail to model the complexity of the human visual system. Others used machine learning to train models on human-labeled datasets, but creating large, high-quality datasets is difficult because people are unable to assign consistent error labels to distorted images. In this paper, we present a new learning-based method that is the first to predict perceptual image error like human observers. Since it is much easier for people to compare two given images and identify the one more similar to a reference than to assign quality scores to each, we propose a new, large-scale dataset labeled with the probability that humans will prefer one image over another. We then train a deep-learning model using a novel, pairwise-learning framework to predict the preference of one distorted image over the other. Our key observation is that our trained network can then be used separately with only one distorted image and a reference to predict its perceptual error, without ever being trained on explicit human perceptual-error labels. The perceptual error estimated by our new metric, PieAPP, is well-correlated with human opinion. Furthermore, it significantly outperforms existing algorithms, beating the state-of-the-art by almost 3A— on our test set in terms of binary error rate, while also generalizing to new kinds of distortions, unlike previous learning-based methods.

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PieAPP: Perceptual Image-Error Assessment Through Pairwise Preference

Robust capacity optimization methods for integrated energy systems considering demand response and thermal comfort

Multi-objective optimal scheduling for CCHP microgrids considering peak-load reduction by augmented ε-constraint method

A novel Modified Fly Optimization Algorithm (MFOA) is proposed for determining the optimal Proportional Integral Derivate controller parameter. The Modified Fly Optimization Algorithm principle is elaborated and utilized to find effectively the optimal PID controller parameter. Some simulation results show that in contrast to the FOA-PID controller, the MFOA-PID controller is more optimal, effective and quick; Further, with respect to the Particle Swarm Optimization (PSO) PID controller, MFOA PID controller shows the close optimized performance in self-tuning PID controller, however, its searching speed is quite faster than PSO-PID.

A Novel Modified Fly Optimization Algorithm for Designing the Self-Tuning Proportional Integral Derivative Controller

A fast nonlocally centralized sparse representation algorithm for image denoising

The linear elastic models of soft tissue are widely used in virtual-reality-based surgery simulation due to their computational efficiency; however, it is well known that these models are only a coarse approximation of the real biological soft tissue. To achieve realistic simulation, the deformable model should incorporate many other tissue properties such as nonlinearity, anisotropy, and viscoelasticity. Among these properties, viscoelasticity is a very important one, and it directly determines the behaviors of the tissue when it is cut, deformed, or torn. In this paper, we proposed to incorporate the property of viscoelasticity into the visual model of soft tissue. One significant advantage of the developed model is its fast computation. Experiments show that the incorporation of nonlinear viscoelasticity makes the simulated tissue look much more realistic than other models, whereas the computation time is increased by only approximately 5% compared with models without the consideration of viscoelasticity.

Shaoping Xu

Papers

Robust capacity optimization methods for integrated energy systems considering demand response and thermal comfort

Multi-objective optimal scheduling for CCHP microgrids considering peak-load reduction by augmented ε-constraint method

A Novel Modified Fly Optimization Algorithm for Designing the Self-Tuning Proportional Integral Derivative Controller

A fast nonlocally centralized sparse representation algorithm for image denoising

A Nonlinear Viscoelastic Tensor-Mass Visual Model for Surgery Simulation