As visual creatures, humans are sensitive to visual signal impairments such as blockiness, blurriness, noisiness, and transmission loss. Thus, I have focused my research on finding how image quality affects user behavior in web applications. Lately, several studies test the effect of low-quality images on web sites. Cornell University  shows that poor pictures negatively impact the user experience, website conversion ratio, how long people stay on the website, and trust/credibility. They use a deep neural network model trained with a publicly available dataset from LetGo.com. The objective is to measure the effect of image quality in sales and perceived trustworthiness. It is found that the predicted image quality is 1.25x more likely to be sold, but they could not measure the effect of image quality to trustworthiness.
The most commonly encountered image distortions are White Noise (WN), Gaussian Blur (GB), JPEG compression, and JP2K compression. For example, the white noise distortion can be caused when taking pictures at night with a mobile, or the gaussian blur if not focusing correctly before taking the shot.
The image quality assessment (IQA) methods are mainly split into two categories (1) reference, and (2) reference-less or blind. A referenced algorithm requires a pristine (a reference that is considered to be good quality) and distorted images to calculate the quality score. Reference-based algorithms are widely used to measure how the quality is compromised after applying processes like image compression, image transmission, or image mosaic. For example, there is a trade-off with image compression; the higher the compression, the lower the perceived image quality. As another example, having an automated way to measure image quality helps companies to define the optimal compression parameters to maximize loading speed without compromising…