No-Reference Perceived Image Quality Algorithm for Demosaiced Images

Abstract

Abstract-Visual image quality assessment (IQA) plays a key role in every multimedia application, as end user to it is a human-being. Real time applications demand no reference (NR) IQA, due to unavailability of the reference image. Today, most of the perceived/visual NR-IQA algorithms developed are for distortions like blur, ringing, and blocking artifacts. Very few are available for color distortions. Visible color distortions, such as false color, and zipper are produced in the demosaiced image due to incorrect interpolation of missing color values. In this paper, state of the art zipper and false color artifact quantification algorithms, general purpose NR-IQA algorithms are evaluated for visual quality assessment of demosaiced images. Separate NR-IQA algorithms are proposed for zipper and false color artifact quantification scores are then combined to obtain final quality score for demosaiced image. Zipper algorithm quantifies zipper artifact by searching for zipper pixels in an image, while false color algorithm finds correlation between local high frequency region's color planes to quantify false color.

Stereo Matching Algorithm Based on Pyramid Double Dynamic Programming

“…Stereo vision technology is mobile robot visual navigation one of the key technologies in the field of [1] . Region-based stereo matching algorithm obtained parallax field that although sufficiently dense [2] , but the parallax figure have more miscellaneous points and poor quality.…”