Research On Image Quality Assessment Based On Structure Similarity Metric

The development of information technology and computer technology has greatly enriched people’s ability to access information and share information. And the people’s daily life is full of convenience and joy brought by high-tech products, especially the Apple, this digital electronic product, is popular around the world in recent years. With the popularity of mobile terminals that can be photographed image, a image becomes a window to a better understanding of the world for everyone, and it is very important for the image processing technology to display image information.Objective digital image quality assessment(IQA) is a vital branch of image processing technology. Its purpose is to measure the quality of the image with the aid of mathematics, engineering and other methods, in order to show the image quality with intuitive digital indicators. Thus people can assess the image quality fastly and simply in real time.The paper starts with the introduction of the basics of image quality assessment. According to different image quality assessment methods, the image quality assessment methods are divided into subjective and objective image quality method. And introduce their basic concepts, scoring methods, and summarize the performance evaluation of commonly used for objective image quality assessment algorithms indicators. Based on the knowledge and understanding of the above, in accordance with the different evaluation algorithm reference source, the objective image quality assessment methods are divided into two categories: non- reference image quality assessment and reference image quality assessment. And the paper has a deep analysis on and a comprehensive summary of the objective digital image quality assessment method of research hotspot and the trend in these two areas in recent years.Secondly, as is known to all that structural similarity assessment metric(SSIM) is highly recognized, so this paper has a deep research and analysis on this thought, and does simulation experiments. And the author learns the last decade various algorithms based on SSIM thought, according to the difference of each algorithm on its process, the algorithm is divided into three types: the spatial domain algorithm, the frequencydomain algorithm, and the integrated algorithm. Moreover, the author shows some experimental data on the performance of each type of algorithm to compare and analyze.Finally, the author has a sharp study on the feature similarity index(FSIM), and finds that this metric cannot distinguish the differences from the objective values obviously, but the diversity is clear through subjective visual, especially for the same distortion with the different degree. To overcome these shortcomings, the author puts forward two improved algorithms combined with squared ideas, and with weighted thoughts and math rating ideas respectively in the post-processing of data: FSIM based on Sudoku grade though(Sudoku-FSIM-Grade) and FSIM based on Sudoku weight though(Sudoku-FSIM-Weight). And we do many experiments whit LIVE2 image library which is usually used for objective digital image quality assessment, and the experimental data show that the two improved algorithms proposed in the paper is not only to maintain the consistency of the original algorithm, but also good to overcome these shortcomings. The algorithms are stable, and are more in line with the characteristics of the human visual system, and have a more accurate IQA, what’ s more, the index is more sensitive.