Neuro Fuzzy Based Filtering for Blocking Artifacts Reduction in Highly Compressed Images

Abstract

In these days, research on artifacts reduction techniques has gained more attention by the researchers because image processing is an emerging area. Main focus of researchers is to design an efficient blocking artifacts reduction approach with concept of Artificial Intelligence (AI). Nowadays, images or videos are extremely effective communication tools as they are more effective in conveying messages than text. Image processing, in general, refers to the manipulation of digital images for purposes such as image compression, image enhancement, image analysis, object detection, and image comprehension. As a result, image processing seeks to extract data from an image for a number of purposes but during the compression process, blocking artifacts are the major coding artifacts caused by high compression. There are a lots of traditional as well as conventional approaches available for reduction of artifacts from image or videos, but most of them are depended on the data types. In this research, neuro- fuzzy based filtering for blocking artifacts reduction in highly compressed images is proposed into three segments. First, an assessment of an enhanced fuzzy inference system for image analysis that preserves image edge is shown. In the field of image analysis, edge detection algorithms have a variety of uses. An edge detection technique is a critical step toward visual system reliability and security, providing a better understanding in a variety of applications such as object recognition, photography, and many other computer-vision applications such as pedestrian detection for vehicles on the road, face detection in biometrics, and video surveillance. We know that edge detection is a scientific technique that is used to provide better image analysis, and many edge detection approaches have already been implemented by researchers in the image processing era for this purpose, but they have not achieved satisfactory results for all types of images that can aid in image analysis. We have offered a comparative assessment