An objective assessment method for a stereoscopic image quality combined with manifold characteristics and binocular characteristics trains a matrix after dimensionality reduction and whitening obtained from natural scene plane images through an orthogonal locality preserving projection algorithm, for obtaining a best mapping matrix. Image blocks, not important for visual perception, are removed. After finishing selecting the image blocks, through the best mapping matrix, manifold characteristic vectors of the image blocks are extracted, and a structural distortion of a distorted image is measured according to a manifold characteristic similarity. Considering influences of an image luminance variation on human eyes, a luminance distortion of the distorted image is calculated according to a mean value of the image blocks. After obtaining the manifold similarity and the luminance similarity, quality values of the left and right viewpoint images are processed with linear weighting to obtain a quality value of the distorted stereoscopic image.
Objective assessment method for stereoscopic image quality combined with manifold characteristics and binocular characteristics