NEW LEARNING FRAMEWORKS FOR BLIND IMAGE QUALITY ASSESSMENT MODEL

The focus of this thesis is on image quality assessment, specifically for problems of assessing the quality of an image blindly or without reference information. There are significant efforts over the last decade in developing objective blind models that can assess image quality as perceived by humans. Various models have been introduced, achieving highly competitive performances and high in correlation with subjective perceptual measures. However, there are still limitations on these models before they can be viable replacements to traditional image metrics over a wide range of image processing applications. This thesis addresses several limitations. The thesis first proposes a new framework to learn a blind image quality model with minimal training requirements, operates locally and has ability to identify distortion in the assessed image. To increase the model’s performance, the thesis then modifies the framework by considering an aspect of human vision tendency, which is often ignored by previous models. Finally, the thesis presents another framework that enable a model to simultaneously learn quality prediction for images affected by different distortion types

Nonparametric Quality Assessment Of Natural Images

In this article,the authors explore an alternative way to perform no-reference image quality assessment (NR-IQA). Following a feature extraction stage in which spatial domain statistics are utilized as features,a two-stage nonparametric NR-IQA framework is proposed.This approach requires no training phase,and it enables prediction of the image distortion type as well as local regions' quality, which is not available in most current algorithms. Experimental results on IQA databases show that the proposed framework achieves high correlation to human perception of image quality and delivers competitive performance to state-of-the-art NR-IQA algorithms

Performance Analysis of Hexagon-Diamond Search Algorithm for Motion Estimation using MATLAB

To achieve a high compression ratio in coding video data, a method known as Motion Estimation (ME) is often applied to reduce the temporal redundancy between successive frames of a video sequence. One of ME techniques, known as Block Matching Algorithm (BMA), has been widely used in various video coding standards. In recent years, many of these BMAs have been developed with similar intention of reducing the computational costs while at the same time maintaining the video signal quality. In this paper, an algorithm called Hexagon-Diamond Search (HDS) is proposed for ME where the algorithm and several fast BMAs, namely Three Step Search (TSS), New Three Step Search (NTSS), Four Step Search (4SS) as well as Diamond Search (DS), are first selected to be implemented onto various type of standard test video sequence using MATLAB before their performances are compared and analyzed in terms of peak signal-to-noise ratio (PSNR), number of search points needed as well as their computational complexity. Simulation results demonstrate that HDS algorithm has speed up other algorithm’s computational work up to 56% while at the same time maintains close performance in terms of PSNR to others

Multi-Task Learning Approach for Natural Images' Quality Assessment

Blind image quality assessment (BIQA) is a method to predict the quality of a natural image without the presence of a reference image. Current BIQA models typically learn their prediction separately for different image distortions, ignoring the relationship between the learning tasks. As a result, a BIQA model may has great prediction performance for natural images affected by one particular type of distortion but is less effective when tested on others. In this paper, we propose to address this limitation by training our BIQA model simultaneously under different distortion conditions using multi-task learning (MTL) technique. Given a set of training images, our Multi-Task Learning based Image Quality assessment (MTL-IQ) model first extracts spatial domain BIQA features. The features are then used as an input to a trace-norm regularisation based MTL framework to learn prediction models for different distortion classes simultaneously. For a test image of a known distortion, MTL-IQ selects a specific trained model to predict the image’s quality score. For a test image of an unknown distortion, MTLIQ first estimates the amount of each distortion present in the image using a support vector classifier. The probability estimates are then used to weigh the image prediction scores from different trained models. The weighted scores are then pooled to obtain the final image quality score. Experimental results on standard image quality assessment (IQA) databases show that MTL-IQ is highly correlated with human perceptual measures of image quality. It also obtained higher prediction performance in both overall and individual distortion cases compared to current BIQA models

Half Car Active Suspension System

This paper presents a new method in modeling an active suspension system for a half-car model in state space form and develop a robust control strategy in controlling the active suspension system. Fuzzy logic is used to control the system. Velocity and displacement of front wheels are taken as input variables of the fuzzy logic controller. Active forces improving vehicle driving, ride comfort and handling properties are considered to be the controller outputs. The controller design is proposed to minimize chassis and wheels deflection when uneven road surfaces, pavement points, etc. are acting on the tires of running cars. Comparison of performance of active suspension fuzzy control system with passive suspension system is shown using Matlab/Simulink simulation. From the result, it shows that active suspension system has better performance than the passive suspension system

PERFORMANCE ANALYSIS OF HEXAGON-DIAMOND SEARCH ALGORITHM FOR MOTION ESTIMATION USING MATLAB

To achieve a high compression ratio in coding video data, a method known as Motion estimation (ME) is often applied to reduce the temporal redundancy between successive frames of a video sequence. One of ME techniques,known as Block Matching Algorithm (BMA), has been widely used in various video coding standards. In recent years, many of these BMAs have been developed with similar intention of reducing the computational costs while at the same time maintaining the video signal quality. In this paper, an Algorithm called HexagonDiamond Search (HDS) is proposed for ME where the algorithm and several fast BMAs, namely Three Step Search (TSS), New Three Step Search (NTSS), Four Step Search (4SS) as well as Diamond Search (DS), are first selected to be implemented onto various type of standard test video sequence using MATLAB before their performances are compared and analyzed in terms of peak signal-to-noise ratio (PSNR),number of search points needed as well as their computational complexity. Simulation results demonstrate that HDS algorithm has speed up other algorithm’s computational work up to 56% while at the same time maintains close performance in terms of PSNR to others

Performance Analysis of Hexagon-Diamond Search Algorithm for Motion Estimation

To achieve a high compression ratio in coding video data, a method known as Motion Estimation (ME) is often applied to reduce the temporal redundancy between successive frames of a video sequence. One of ME techniques, known as Block Matching Algorithm (BMA), has been widely used in various video coding standards. In recent years, many of these BMAs have been developed with similar intention of reducing the computational costs while at the same time maintaining the video signal quality. In this paper, an algorithm called Hexagon-Diamond Search (HDS) is proposed for ME where the algorithm and several fast BMAs, namely Three Step Search (TSS), New Three Step Search (NTSS), Four Step Search (4SS) as well as Diamond Search (DS), are first selected to be implemented onto various type of standard test video sequence using MATLAB before their performances are compared and analyzed in terms of peak signal-to-noise ratio (PSNR), number of search points needed as well as their computational complexity. Simulation results demonstrate that HDS algorithm has speed up other algorithm’s computational work up to 56% on average while at the same time maintains close performance in terms of average PSNR to others

The Development Of Rectangular Waveguide Bandpass Filter

The design and development of rectangular waveguide bandpass filter is presented in this paper. The principle of operation of the filter is that the posts act as shunt inductive discontinuities and the sections of the waveguide between the posts are half waveguide resonators. The bandpass operated at X band frequencies with 1 GHz bandwidth. Pass band is from 8 GHz to 9 GHz. Insertion loss achieved is less than 0.3 dB and return loss of more than 15 dB. Lower cut off frequency at 8 GHz is more selective than the upper cut off frequency at 10.5 GHz, which is 40dB and 20dB respectively. 3D simulator was used to model the structure and simulation results showed very good response