Empirical Study on Quantitative Measurement Methods for Big Image Data : An Experiment using five quantitative methods

Context. With the increasing demand for image processing applications in multimedia applications, the importance for research on image quality assessment subject has received great interest. While the goal of Image Quality Assessment is to find the efficient Image Quality Metrics that are closely relative to human visual perception, from the last three decades much effort has been put by the researchers and numerous papers and literature has been developed with emerging Image Quality Assessment techniques. In this regard, emphasis is given to Full-Reference Image Quality Assessment research where analysis of quality measurement algorithms is done based on the referenced original image as that is much closer to perceptual visual quality. Objectives. In this thesis we investigate five mostly used Image Quality Metrics which were selected (which includes Peak Signal to Noise Ratio (PSNR), Structural SIMilarity Index (SSIM), Feature SIMilarity Index (FSIM), Visual Saliency Index (VSI), Universal Quality Index (UQI)) to perform an experiment on a chosen image dataset (of images with different types of distortions due to different image processing applications) and find the most efficient one with respect to the dataset used. This research analysis could possibly be helpful to researchers working on big image data projects where selection of an appropriate Image Quality Metric is of major significance. Our study details the use of dataset taken and the experimental results where the image set highly influences the results. Methods. The goal of this study is achieved by conducting a Literature Review to investigate the existing Image Quality Assessment research and Image Quality Metrics and by performing an experiment. The image dataset used in the experiment is prepared by obtaining the database from LIVE Image Quality Assessment database. Matlab software engine was used to experiment for image processing applications. Descriptive analysis (includes statistical analysis) was employed to analyze the results obtained from the experiment. Results. For the distortion types involved (JPEG 2000, JPEG compression, White Gaussian Noise, Gaussian Blur) SSIM was efficient to measure the image quality after distortion for JPEG 2000 compressed and white Gaussian noise images and PSNR was efficient for JPEG compression and Gaussian blur images with respect to the original image. Conclusions. From this study it is evident that SSIM and PSNR are efficient in Image Quality Assessment for the dataset used. Also, that the level of distortions in the image dataset highly influences the results, where in our case SSIM and PSNR perform efficiently for the used database.