Visually Lossless Perceptual Image Coding Based on Natural- Scene Masking Models

Perceptual coding is a subdiscipline of image and video coding that uses models of human visual perception to achieve improved compression efficiency. Nearly, all image and video coders have included some perceptual coding strategies, most notably visual masking. Today, modern coders capitalize on various basic forms of masking such as the fact that distortion is harder to see in very dark and very bright regions, in regions with higher frequency content, and in temporal regions with abrupt changes. However, beyond these obvious forms of masking, there are many other masking phenomena that occur (and co-occur) when viewing natural imagery. In this chapter, we present our latest research in perceptual image coding using natural-scene masking models. We specifically discuss: (1) how to predict local distortion visibility using improved natural-scene masking models and (2) how to apply the models to high efficiency video coding (HEVC). As we will demonstrate, these techniques can offer 10–20% fewer bits than baseline HEVC in the ultra-high-quality regime

Performance Analysis of eXogenous Kalman Filter for INS/GNSS Navigation Solutions

There are several methods of fusing data for navigation solutions using Inertial Navigation System (INS) aided by Global Navigation Satellite System (GNSS). The most used solutions are nonlinear observer (NLO) and extended Kalman filter (EKF) of various architectures. EKF based estimation methods guarantees sub-optimal solutions but not stability, on the contrary NLO based estimation guarantees stability but not optimality. These complimentary features of EKF and NLO has been combined to design an eXogenous Kalman filter (XKF) where the estimate from the NLO is used as an exogenous signal to calculate the linearized model of the EKF. The performance of the designed XKF is tested on real flight test data collected using a Slingsby T67C ultra-light aircraft. The results show that during the outage of GNSS, in some cases the divergence of position estimates using XKF is lower compared to EKF and NLO, however no clear benefit is achieved

Dynamic Inversion-Based Flare Control Law for Autonomous Helicopter Autorotation

A novel trajectory generation and control architecture for fully autonomous autorotative flare is proposed that combines rapid path generation with model-based control. The trajectory generation component uses optical Tau theory to compute flare trajectories for both longitudinal and vertical vertical speed. These flare trajectories are tracked by a nonlinear dynamic inversion (NDI) control law. One convenient feature of NDI is that it inverts the plant model in its feedback linearization loop, which eliminates the need for gain scheduling. However, the plant model used for feedback linearization still needs to be scheduled with the flight condition. This key aspect is leveraged to derive a control law that is scheduled with linearized models of the rotorcraft flight dynamics obtained in steady-state autorotation while relying on a single set of gains. Computer simulations are used to demonstrate that the NDI control law is able to successfully execute autorotative flare in the UH-60 aircraft. Autonomous flare trajectories are compared to piloted simulation data to assess similarities and discrepancies between piloted and automatic control approaches. Trade studies examine which combinations of downrange distances and altitudes at flare initiation result in successful autorotative landings

Gust Load Alleviation System for BWB (Blended Wing Body) Flexible Aircraft

A new BWB concept aircraft was developed to meet the ACARE 2020 vision, NACRE-FW1. A patented feedforward controller was designed earlier at EADS Innovation Works to alleviate the gust loading, the controller was robust over various gust lengths and mass cases. The feedforward controller performed extremely well in reducing wing root moments for gusts longer than 60.9 meter, but the controller deteriorated the original aircraft performance by giving rise to the wing root moments for gust lengths shorter than 60.9 meter. This caused significant damage to the aircraft and affected the sizing issues of the wing root joints. This paper focuses on designing an additional robust control loop which would work together with patented Feedforward controller to improve the performance at shorter gust lengths. Emphasis was given on the reduction of wing root moments keeping the overall stability of the aircraft to an acceptable level. The paper originally contributes towards realising the use of an extra control loop to make the feedforward controller insensitive at short gust lengths, and further improving performance at longer gust lengths. For the control design, the non linear actuators model of the BWB aircraft was linearised with $2nd$ order approximation. New GLAS controller was designed to work together with feedforward controller using different design techniques namely, nominal SISO and modern Linear Quadratic Regulator and Hinfinity controller. The result shows that the nominal SISO controller significantly improves the GLAS's performance in terms of reduction of wing root moments compared to LQ and Hinfinity controller, which provides structural benefits.Validerat; 20131007 (global_studentproject_submitter

Gust Load Alleviation System for BWB (Blended Wing Body) Flexible Aircraft

A new BWB concept aircraft was developed to meet the ACARE 2020 vision, NACRE-FW1. A patented feedforward controller was designed earlier at EADS Innovation Works to alleviate the gust loading, the controller was robust over various gust lengths and mass cases. The feedforward controller performed extremely well in reducing wing root moments for gusts longer than 60.9 meter, but the controller deteriorated the original aircraft performance by giving rise to the wing root moments for gust lengths shorter than 60.9 meter. This caused significant damage to the aircraft and affected the sizing issues of the wing root joints. This paper focuses on designing an additional robust control loop which would work together with patented Feedforward controller to improve the performance at shorter gust lengths. Emphasis was given on the reduction of wing root moments keeping the overall stability of the aircraft to an acceptable level. The paper originally contributes towards realising the use of an extra control loop to make the feedforward controller insensitive at short gust lengths, and further improving performance at longer gust lengths. For the control design, the non linear actuators model of the BWB aircraft was linearised with $2nd$ order approximation. New GLAS controller was designed to work together with feedforward controller using different design techniques namely, nominal SISO and modern Linear Quadratic Regulator and Hinfinity controller. The result shows that the nominal SISO controller significantly improves the GLAS's performance in terms of reduction of wing root moments compared to LQ and Hinfinity controller, which provides structural benefits.Validerat; 20131007 (global_studentproject_submitter

Visual Masking in Natural Scenes: Database, Models, and an Application to Perceptual Image Coding

Studies of visual masking have provided a wide range of important insights into the processes involved in visual coding. However, very few of these studies have employed natural scenes as masks, and little is known on how natural scenes affect visual detection thresholds. This report describes a study designed to obtain local contrast detection thresholds for a database of natural images. Via a three-alternative forced-choice experiment, thresholds were measured for detecting 3.7 cycles/degree vertically oriented log-Gabor noise targets placed within an 85x85-pixels patch (1.9 degrees patch) drawn from 30 natural images. Thus, for each image, a masking map was obtained in which each entry in the map denoted the RMS contrast threshold for detecting the log-Gabor noise target at the corresponding spatial location in the image. Qualitative observations showed detection thresholds were affected by several patch properties such as visual complexity, fineness of textures, sharpness, and overall luminance. The quantitative analysis showed that except for the sharpness measure (Pearson correlation coefficient, CC of 0.7), the other tested low-level mask features showed a weak correlation (CC less than 0.52) with the detection thresholds. Three computational models of visual masking were used to predict the thresholds. The first model was a feature-regression model, the second model was an optimized gain-control model, and the third model consisted a three-layer convolutional neural network (CNN) architecture. In terms of CC and RMSE, the gain-control model performed the best with overall CC and RMSE of 0.83 and 5.2 dB, respectively. However, in terms of execution time, the CNN model performed the best with an average execution time of 5 seconds per image, compared to 40 seconds and 66 seconds for the feature-based and gain-control model, respectively. Furthermore, a structural facilitation model is proposed to improve the prediction for patches containing recognizable structures. Prediction performance increased for images with structures: for image geckos, child_swimming, and foxy the CC became 0.77, 0.87, and 0.63 from 0.68, 0.85, and 0.58, respectively. Moreover, using a subjective local-quality-assessment experiment it was found that masking predicted the local quality scores more than 95% correctly above 15 dB threshold within 5% subject scores. Finally, a block based quantization scheme was proposed for still-image compression for high-efficiency-video-coding standard using the masking model. The compression gain was around 23%, and 30% for at threshold, and 1 dB beyond threshold, respectively.Electrical Engineerin