The Concave n-Square Salient Wood Image-based Quality Assessment

Abstract

We make an offer of a state-of-the-art method of the deep salient wood image-based quality assessment (DS-WIQA) for no-reference image appraisal. We explore a five-layer deep convolutional neural network (DCNN) for the salient wood image map. The DS-WIQA uses the concave n-square method. The outcomes allow that DS-WIQA model has a greater achievement on Zenodo and Lignoindo datasets, respectively. We appraise a salient wood image map by extracting in small wood image patches. The DS-WIQA has an admirable performance of other recent methods on Zenodo and Lignoindo datasets, respectively. DS-WIQA outdoes other recent techniques by 14.29% and 19.96% more advanced than other techniques with respect to SROCC and LCC measurement, respectively. DS-WIQA shows up to be more significant than the other DCNN methods.