Research On Image Quality Assessment Based On Sparise Processing

On one hand,image quality degrades because of imperfect acquisition,compression coding,storage and transition faults which increases the difficulty for human recognition.On the other hand,in research fields on digital image processing,processed image quality comparison directly demonstrates the performance of algorithms.Also,image quality is an essential assessment standard and can be embedded into image processing systems for feedback to optimize parameters.However,since experiments are complicated,sensitive to experimental environment,and unable to be duplicated,subject image quality assessment(IQA)by human is difficult to be applied in large application scenarios.Objective IQA,as evaluation algorithms to predict image quality in accordance with objective assessment results,starts to draw researchers' wide attention.Sparse processing is a hot research topic in signal processing.Sparse processing methods are efficient to deal with large amount of complex data with less non-zero variables to represent original data and this makes it easy to be analyzed.In this.paper,sparse processing theory is discussed and IQA methods based on sparse processing is proposed.The summary is given below.(1)A brief survey on sparse processing theory is done.The theory and application of kernel independent component analysis is studied.A full-reference objective IQA method is proposed based on kernel independent component analysis based on the fact that kernel independent component analysis can do non-linear decomposition in reproduced Hilbert kernel space.The proposed method uses correlation between two sets of features from reference image and distorted image to map onto evaluated quality value based on natural image statistics.Experimental results demonstrate that features extracted based on proposed methods can predict image quality well.(2)During experiment,it is found out that the independency of components among one set directly affects the precision of image quality prediction.This leads to the question that how to extract more independent and effective features relates to the performance of methods based on independent component analysis.Block matching methods using frequency domain distance as matching condition select image samples efficiently.After further decomposition of principal component analysis and kernel independent component analysis,the precision is improved obviously.At the end of this paper,the thought and key to apply sparse processing to IQA framework is summarized.Sparse processing is promising in IQA research and the proposed methods in this paper based on component analysis is summarized.And future work is given.