A No-Reference Image Quality Assessment Metric for Wood Images

Abstract

Image Quality Assessment (IQA) is a vital element in improving the efficiency of an automatic recognition system of various wood species. There is a need to develop a No-Reference IQA (NR-IQA) system as a perfect and distortion free wood images may be impossible to be acquired in the dusty environment in timber factories. To the best of our knowledge, there is no NR-IQA developed for wood images specifically. Therefore, a Gray Level Co-Occurrence Matrix (GLCM) and Gabor features-based NR-IQA, GGNR-IQA metric is proposed to assess the quality of wood images. The proposed metric is developed by training the support vector machine regression with GLCM and Gabor features calculated for wood images together with scores obtained from subjective evaluation. The proposed IQA metric is compared with a widely used NR-IQA metric, Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Full Reference-IQA (FR-IQA) metrics. Results shows that the proposed NR-IQA metric outperforms the BRISQUE and the FR-IQA metrics. Moreover, the proposed NR-IQA metric is beneficial in wood industry as a distortion free reference image is not needed to evaluate the wood images.

Development of Image Quality Assessment (IQA) For Haze Prediction

“…Firstly, the Mean Subtracted Contrast Normalized (MSCN) of the hazy images were calculated [6]. Then, two types of Gaussian distribution functions were incorporated in this study to accommodate the diverse characteristics of MSCN coefficient, namely the Generalized Gaussian Distribution (GGD) and Asymmetric Generalized Gaussian Distribution (AGGD) [5] .…”