Subjective evaluation of tone-mapped images is tedious and time-consuming; therefore, it is desirable to have algorithms for automatic quality assessment. Many full-reference and blind metrics have been developed for this purpose, but their performance is generally evaluated on limited benchmark datasets. This leaves a possibility that the observed performance of the metric could be due to overfitting, and it might indeed not perform well for all scenes. In this work, we propose a novel framework using population-based metaheuristics to evaluate the performance of these metrics without requiring any subjectively evaluated reference dataset. The proposed algorithm does not modify the individual image pixels, instead, the tone-mapping curve is modified to synthesize realistic tone-mapped images for evaluation. Moreover, it is not required to know the underlying model of the evaluated metric, which is treated just like a black box and can be replaced by any other metric seamlessly. Therefore, any new metrics designed in the future can also be easily evaluated by simply replacing just one module in the proposed evaluation framework. We evaluate six existing metrics and synthesize images to which the metrics fail to assign appropriate scores for visual quality. We also propose a method to rank the relative performance of evaluated metrics, through a competition in which each metric tries to find the errors in the scores given by other metrics.

High dynamic range (HDR) images require tone-mapping to be viewed on low dynamic range (LDR) displays. The performance of tone-mapping algorithms can be evaluated through a subjective study in which participants based on their liking rank or score tone-mapped images (TMIs). Subjective evaluation can be painstakingly slow; therefore, several quantitative metrics have been proposed for objective evaluation. This paper presents a new robust metric that uses 16 features, measuring the loss of color, contrast, brightness, and structure, extracted from the test TMI and the reference HDR image. The effect of these attributes on image quality is investigated and combined into a single score in the [0, 1] range describing the quality of TMI. We validate the performance of the proposed metric by comparing it with 24 existing state-of-the-art metrics. The study uses two subjective datasets of TMIs, including one existing benchmark dataset and a new proposed dataset comprising HDR images of a variety of scenes, and a dataset of traditional images not generated through tone-mapping. In these studies, our method shows the highest correlation with subjective scores for both datasets of TMIs and remains in the second position for the dataset of traditional images.

The flexibility and rapid mobility of drones make them ideal for Internet of Things (IoT) applications, such as traffic control and data collection. Therefore, the autonomous navigation of 5G drones in unknown and dynamic environments has become a major research topic. Current methods rely on sensors to perceive the environment to plan the path from the start point to the target and to avoid obstacles; however, their limited field of view prevents them from moving in all directions and detecting and avoiding obstacles. This article proposes the deep learning (DL)-based autonomous navigation of 5G drones. This proposal uses sensors capable of perceiving the entire environment surrounding the drone and fuses sensor data to detect and avoid obstacles, plan a path, and move in all directions. We trained a convolution neural network (CNN) using a novel dataset we created for drone ascent and passing over obstacles, which achieved 99% accuracy. We also trained artificial neural networks (ANNs) to control drones and achieved a 100% accuracy. Experiments in the Gazebo environment demonstrated the efficiency of sensor fusion, and our proposal was the only one that perceived the entire environment, particularly above the drone. Furthermore, it excelled at detecting U-shaped obstacles and enabling drones to emerge from them.

The increasing popularity of drones for Internet of Things (IoT) applications has led to significant research interest in autonomous navigation within unknown and dynamic environments. Researchers are utilizing supervised learning techniques that rely on image datasets to train drones for autonomous navigation, which are typically used for rescue, surveillance, and medical aid delivery. Current datasets lack data that allow drones to navigate in a 3D environment; most of these data are dedicated to self-driving cars or navigation inside buildings. Therefore, this study presents an image dataset for training drones for 3D navigation. We developed an algorithm to capture these data from multiple worlds on the Gazebo simulator using a quadcopter. This dataset includes images of obstacles at various flight altitudes and images of the horizon to assist a drone in flying at an appropriate altitude, which allows it to avoid obstacles and prevents it from flying unnecessarily high. We used deep learning (DL) to develop a model to classify and predict the image types. Eleven experiments performed with the Gazebo simulator using a drone and a convolution neural network (CNN) proved the database’s effectiveness in avoiding different types of obstacles while maintaining an appropriate altitude and the drone’s ability to navigate in a 3D environment.