Blind Degraded Image Quality Assessment Based On Improved Ant Colony Algorithm

With rapid development of computer hardware and software,especially the intelligent machine vision technology,network multimedia applications related to video has played a key role in video surveillance and network television.It is needed to emulate intelligent human vision to evaluate image quality without the need of the original scene information.Usually,blur and noise are the most important two kinds of distortion that affect image quality.This thesis focuses on image noise and image blur distortion for evaluating the quality of degraded image.The thesis also focuses on ant colony algorithm.More specifically,the proposecd approaches use simple intelligent individual co-evolution,and realizes intelligent and gets rid of the statistical method based on the degradation model and estimate accuracy.The results can be used to optimize the related image processing algorithms,and experiment in security monitoring.The main contribution of this thesis includes:1)Proposed image feature consistency evaluating for degracded image blind estimation.Two image quality evaluation algorithms have been proposed using(i)color feature consistency for degraded image blind noise estimation;and(ii)texture feature consistency for degraded image blind noise estimation.The color feature consistency for image noise estimation method is based on the similarity characteristics of noise parameter estimation method.Inspired by the fact that image block between pixels has a certain consistency principle,a block-based homogeneity measure is proposed to estimate the statistics(e.g.,variance)of the noise incurred in the image,based on adaptively selected homogeneous image regiorns.Then,an image quality assessment approach is proposed by exploiting the above-mentionedc estimated noise variance,along with the visual masking effect of the human visual system.Based on texture feature consistency for degraded image blind noise estimation utilizes the eigenvalue analysis to mathematically derive a new noise level estimator based on weak-textured image patches.Furthermore,a new texture strength measure is proposed to adaptively select weak-textured patches from the noisy image.Experimental results are provided to demonstrate that the proposed two image noise level estimation approaches yields superior accuracy and stability performance to that of conventional noise level estimation approaches.2)Proposed deformable ant colony optimization for degradecd image blind estimation.Combined with the image feature consistency,a deformable ACO(Deformable Ant Colony Optimization,DACO)approach is proposecd to adcaptively adjust the ant size for image block selection.The proposed DACO approach consicders that based on image color character consistency of ant food and the size of the ant is adjustable during foraging.For the smooth image blocks,more pheromone is deposited,and then the size of ant is increased.Therefore,this strategy enables the ants to have dynamic food-search ability,leading to more accurate homogeneous block selection.Furthermore,the regression analysis is usecd to obtain image quality score by exploiting the above-estimated noise statistics.Experimental results are provicdecd to justify that the proposed approach outperforms conventional approaches to provide more accurate noise statistics estimation and achieve a consistent image quality evaluation performance for both the artificially-generated and real-world noisy images.3)Proposed modified ant shortest path for degraded video blind assessment.As a Chinese saying goes,"In Shangri-La,the best scenery is on the road,not in the national park".Instead of tedious searching for the final destination,this thesis tempts to study and reveal that the path itself is more meaningful than the destination.The proposed modified ant shortest path(MASP)algorithm uses the path information of the ant colony optimization(ACO).The contribution of the proposed approach is two-folod.First,the proposed approach utilizes a number of artificial ants to move on a 2-D graph for constructing the path information,and calculates the ants' movement path driven by the shortest path strategy.Second,several path statistics metrics are proposed to evaluate the image quality.Experimental results are provided to demonstrate that the proposed image quality assessment approach is effective for both benchmark image database and real-world noisy video.4)Proposed ants competition model algorithm for degraded image blind assessment.Ants in the feed will appear in the process of cluster phenomenon,and exchange useful information between groups and have competitive relationship between populations,which encourages the co-evolution of populations.According to the population competition idea,ant,competition model algorithm(ACMA)for degraded image blind estimation approach is proposed in this paper.Firstly,two types of ants are set to compete against each other,according each type of ant food set goals(foreground/background),to find their own food.One class of ant food is set the strong image edge while the other one is set for the smooth area.Two types of ants compete for food.Finally,the final result can be obtained by the two types of ants pheiromone competition.The experimental results show that the proposed method can achieve a consistent image quality evaluation performance.