An Enhanced LSDBIQ Algorithm for Full Reference Image Quality Assessment for Multi Distorted Images

Image processing is an emerging technology as image is used in various fields like medical and education. Images may corrupt due to the various categories of noises. Image quality reduces because of the image acquisition or transmission. Noise reduction is the main focus to retain the quality of the image. For the removal of this noise, there are various techniques and filters.  Before applying further processing on the image, noise should be removed from the image. In t his paper we deal with with a practical and effectual IQA model, called LSDBIQ (local standard deviation based image quality) . This metric is examined on a well known database MDID (multi distorted image dataset). Exploratory results  manifest that this metric perform better than alternative techniques for the assessment of image quality and have very low computational complexity.