Binocular responses for no-reference 3d image Quality assessment - 2016 PROJECT TITLE : Binocular responses for no-reference 3d image Quality assessment - 2016 ABSTRACT: Perceptual quality assessment of distorted three-dimensional (3D) images has become a basic nevertheless challenging issue in the field of 3D imaging. During this paper, we propose a general-purpose blind/no-reference (NR) 3D image quality assessment (IQA) metric that utilizes the complementary local patterns (the local magnitude pattern and the proposed generalized local directional pattern) of binocular energy response (BER) and binocular rivalry response (BRR). The main technical contribution of this analysis is that binocular visual perception and local structural distribution are considered for NR 3D-IQA. Additional specifically, the metric simulates the binocular visual perception using BER and BRR. Subsequently, the local patterns of the binocular responses' encoding maps are used to make varied binocular quality-predictive features, which can modification in the presence of distortions. When feature extraction, we have a tendency to use k-nearest neighbors-based mostly Machine Learning to drive the overall quality score. We tend to tested our proposed metric against 2 publicly offered 3D databases; these tests make sure that the proposed metric's results consistently align with human subjective judgments. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Learning (Artificial Intelligence) Local Binary Pattern Image Processing 3D Image Quality Assessment Binocular Energy Response Binocular Rivalry Response Backward registration based Aspect ratio similarity(ARS) For image retargeting quality assessment - 2016 Blind image quality assessment based on Multichannel features fusion and label transfer - 2016
